{"paper_id":"0ca466ee-9fc1-489e-9c7a-c4ee26fa8f31","body_text":"Ixodes ricinus in Ireland: exploring the links between environmental factors, host species activity and tick abundance in an area of Europe with limited potential vertebrate hosts | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Ixodes ricinus in Ireland: exploring the links between environmental factors, host species activity and tick abundance in an area of Europe with limited potential vertebrate hosts Ríona Walsh, Mike Gormally, Christopher Williams, Orla Hamilton, and 2 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-4848879/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Background: Ixodes ricinus (Linnaeus 1758) vectors several important diseases in Europe, and the nymphal abundance in an area is an important factor determining tick bite risk. While interactions between abiotic, habitat, and vertebrate host factors and this tick species are generally well understood in continental Europe, this is not the case in Ireland, a highly fragmented and vertebrate depauperate region of Europe. This study examines the abiotic, habitat and host factors predicting nymphal abundance in such a setting. Our findings may provide insights for possible future changes in I. ricinus vector ecology on continental Europe given current predictions of future vertebrate diversity loss. Methods: 15 woodland sites in Ireland were surveyed over three years (2020-2022) wherein abiotic and habitat factors were determined and tick abundance recorded. Concurrently, mammal and birdsong activity data were collected for each site across multiple visits. Generalised linear mixed models were used to identify the most important factors predicting I. ricinus abundance. Results: Nymphal I. ricinus abundance was driven by seasonality, with peak abundance occurring in April. Abiotic and habitat factors featured less than expected in models predicting nymphal abundance, but mean minimum winter temperature was found to have an inverse predictive relationship with adult tick abundance. While I. ricinus nymphs were significantly more abundant at sites where deer were present, at visit level, there was an inverse predictive relationship between deer activity events the week of a site visit and nymphal abundance. Modelling individual host species as predictors of nymphal abundance also identified increased mean robin birdsong events for the previous year to be a predictor of decreased nymphal abundance. Conclusions: Seasonality predicted nymphal tick abundance more robustly than any other abiotic variable. Seasonality was also the driving factor behind the relationships seen between deer activity and nymphal abundance. This highlights the importance of understanding the seasonal changes in dynamics between I. ricinus abundance and host activity, a less well-studied area. Furthermore, the identification of European robin as a predictor of nymphal abundance in woodland sites confirms the important relationship between passerine bird species and I. ricinus in Ireland. Ixodes ricinus abundance tick bite risk abiotic factors host factors avian hosts Figures Figure 1 Figure 2 Figure 3 Background Ticks, vectors to numerous pathogens, are found across a wide geographical range, and, in Europe, tick abundance, distribution, and tick-borne disease incidence are thought to be increasing [ 1 ]. This increase is driven by changes in climate, habitat degradation, and vertebrate host diversity and community composition [ 2 , 3 ]. Of the tick species found in Europe, Ixodes ricinus is the most significant in terms of human impact as it can transmit Borrelia burgdorferi s.l., the bacterial complex associated with Lyme borreliosis [ 4 ], and tick-borne encephalitis virus (TBEV) [ 1 ]. While I. ricinus spends the majority of its life off-host, it requires a blood-meal from a host to moult from one life stage (larva, nymph, adult) to the next [ 5 , 6 ]. As tick bite risk increases with increasing nymphal abundance, understanding the factors impacting nymphal abundance in an area is essential to determine the risk of disease to humans [ 7 , 8 ]. Seasonality affects nymphal abundance, as I. ricinus questing behaviour occurs most commonly during the spring and summer months [ 9 ]. As such, a dataset containing tick abundance across multiple time points and multiple sites is useful in assessing drivers of abundances in tick populations while taking seasonality into account [ 10 ]. Ticks require a high level of humidity (> 80%) to survive off-host [ 11 ], and therefore dry climatic conditions with lower relative humidity generally reduce the questing activity of I. ricinus [ 12 ]. However, in Scotland, which has a relatively wet and cool climate, fewer questing nymphs are also found when weather and/or habitat conditions are particularly wet; therefore, wet conditions may also be a limiting factor in terms of activity in some parts of the range of I. ricinus [ 13 ]. Another important factor in the activity levels and survival of I. ricinus is temperature [ 14 ]. However, I. ricinus in differing regions of Europe have been found to be active at varying temperatures, with ticks from more northern areas of Europe being active at lower temperatures than those in southern regions [ 15 ]. Environmental and micro-habitat factors such as soil type [ 16 ], habitat type, leaf litter, and vegetation structure also impact nymphal abundance in some areas [ 13 , 17 , 18 ]. Globally, forests are frequently cited as being the preferred habitat of I. ricinus [ 17 ], with highest abundance at forest edges [ 1 ]. Host availability also plays a major role in nymphal abundance. Birds and small mammals are thought to be important hosts for larvae and nymphs, while sheep ( Ovis aries ) and deer ( Cervidae ) feed adults and are considered reproduction hosts [ 19 , 20 ]. In European disease systems, rodent density [ 3 ], deer density or activity [ 21 , 22 ], and vertebrate predator activity [ 3 ] have often been cited as impacting nymphal abundance. However, there are differences between regions as to the main host drivers of nymphal abundance. In the Netherlands, for example, rodent numbers are positively correlated with nymphal abundance [ 3 ]. However, in Scotland, the opposite seems to be the case, with a study in Scotland reporting more nymphs in sites with low rodent abundance [ 23 ]. There is similarly conflicting evidence in the literature as to the nature of the relationship between deer density and nymphal abundance, with one study in Scotland finding a positive [ 13 ] correlation, while another study in Scotland found no correlation between deer density and nymphal abundance [ 24 ]. Add a sentence here about what we know regarding adult ticks? Ireland represents an area within the range of I. ricinus that has year-round high humidity, a temperate climate, highly fragmented habitats, low woodland cover [ 25 ], and is host-species poor [ 26 ]. Previous studies have hypothesised that birds possibly play a more important role as tick hosts in Ireland than elsewhere in Europe [ 27 ], potentially due to the lower availability of small mammal hosts [ 27 ]. This hypothesis is based on the fact that the most dominant genospecies of B. burgdorferi s.l . in Ireland are Borrelia garinii and Borrelia valaisiana , which are bird-related genospecies [ 27 , 28 ]. The confirmation of this postulation would have important implications for understanding changes in I. ricinus ecology in the context of mammal diversity loss in continental Europe. However, to the authors’ knowledge, no previous studies have focussed on the relationships between individual bird species and I. ricinus ticks in Ireland. Furthermore, although extensive primary studies assessing nymphal infection prevalence in Ireland have been undertaken [ 28 ], and tick-presence is also known to be more likely in the west of Ireland [ 28 ], an in-depth analysis combining the effects of abiotic, habitat, and host-related factors on questing nymphal tick abundance, and therefore tick bite risk, has never (to the authors’ knowledge) been undertaken in Ireland. The results of such a study in Ireland will help us to understand the factors affecting tick bite risk in particularly host-depauperate, and highly fragmented areas of the distribution of I. ricinus . Therefore, the aims of this study are to: Identify key abiotic and biotic factors that predict nymphal/adult I. ricinus abundance in woodland sites in Ireland. Assess the relative role of bird and mammal species as predictors of nymphal tick abundance within a highly fragmented and mammal host-species poor region of Europe. Methods Site selection Fifteen sites in the east and west of Ireland, covering a range of woodland types, sizes, and distances from urban centres were selected (Fig. 1 ; Additional file 1, Table S1 ;). In Year 1 (2020) all sites were sampled for adult and nymphal ticks four times between April and November (season of peak I. ricinus activity) and those sites which yielded ticks on multiple visits were subsequently sampled four times in both Years 2 (2021) and 3 (2022). Sites on which no or very few (two or fewer) ticks were found in Year 1 (2020) were sampled twice in Year 2 (2021) only, with the exception of Seanaphéistín, which was re-visited four times in 2021 and 2022 for the purposes of other analyses outside the scope of this study (Table 1 ; Additional File 1, Table S2 ). Woodland edges were selected for tick sampling at each site since tick abundance at ecotones is thought to be greatest (1) and all repeat visits (tick and host data collection) were at the same locations within each woodland. Table 1 Characteristics of all fifteen fieldwork sites, listed in alphabetical order Name Type Size (Ha) + Distance to urban fabric (Km) + County Barna Woods a Deciduous Woodland size not given in dataset < 1 Galway Crone Wood a Coniferous 790 6 Wicklow Deerpark a Deciduous 63 5 Sligo Derroura Forest b Mixed 1257 28 Galway Derrycrag Woods b Deciduous 498 38 Galway Glen of the Downs a Deciduous 488 < 1 Wicklow Hazelwood a Deciduous 176 < 1 Sligo Knocknarea a Coniferous 90 4 Sligo Merlin Woods a Mixed 40 < 1 Galway Newvillage Forest b Coniferous 412 23 Galway Phoenix Park Oldtown wood a Deciduous Woodland size not given in dataset < 1 Dublin Portumna b Mixed 439 41 Galway Seanaphéistín b Coniferous 3366 19 Galway Slishwood b Mixed 558 5 Sligo Ticknock a Coniferous 248 < 1 Dublin + as per Corine 2018 dataset a Sampled four times per year in Year 1 (2020) and twice in Year 2 (2021) only b Sampled four times per year in Years 1 (2020), 2 (2021) and 3 (2022) Tick collection Tick collection utilitsed the standard tick dragging methodology as described by Vectornet [ 29 ] for tick abundance measurement. This method involves dragging a 1m 2 white cotton fabric along a 5m transect, raising it for 5m and then dragging it again for another 5m until a total of 30 drags have been completed. For efficiency, all adults and nymphal ticks were removed from the drag material after every 5th drag and stored in labelled vials containing 70% ethanol at 4°C. Nymphal and adult tick abundance per visit (i.e. for 30 x 5m drags = 150m 2 ) was calculated. In accordance with Vectornet recommendations, ticks were considered absent from the study area if no tick was collected after 60 x 5m 2 drags [ 29 ]. All ticks were identified to species level with reference to the key by Arthur [ 30 ] using a light microscope (Olympus CX40) at 100-400X. Mammal data collection Relative vertebrate host activity was assessed at each site using mammal camera traps and birdsong analysis. Two trail cameras were placed at each site for a duration of six nights at a time that coincided with a tick collection visit. Cameras were placed (0.5m – 1m in height [ 31 ]), close to the tick collection transect, facing away from any human-made paths. Cameras were set to photograph capture mode to ensure battery was conserved throughout the deployment period. Of the two cameras deployed for each six-night period, one was baited with dry cat food to target small to medium mammals, while the other was pointed towards an animal trail where one was visible [ 32 ]. Bait was contained within a metal tea strainer hung from a branch approximately 1.5m high and approximately 2- 4m from the camera [ 31 ]. The number of replicates of camera trap deployments for each site during each study season can be seen in Additional File 1, Table S2 . Relative host activity was calculated based on the number of captures of each vertebrate species per camera trap night, and recorded as ‘host activity events’ [ 31 ]. No distinction was made between the deer species captured by camera trap footage as a UK study has demonstrated that different deer species contribute similar effects to tick abundance and infection prevalence [ 23 ]. Camera traps were deployed in tandem with tick collection site visits so that analysis of vertebrate host activity could be linked with that of tick abundance data. Mean host activity at the site during the previous study year was also calculated for sites visited in 2021 and 2022, to reflect the possibility that host activity may have a delayed effect on tick numbers, given the extended periods between I. ricinus bloodmeals [ 5 ]. Birdsong Data Collection Birdsong, recorded to determine potential avian hosts of ticks at the woodland sites, was captured via 15-minute ambient bioacoustic recordings taken during tick sampling at each site. The recorder was placed at ground level beside the tick collection transect (see Additional File 1, Table S2 for number of replicates of birdsong recordings). The resultant soundscape was analysed using BirdNet (33, 34). BirdNet was validated for use in the setting of Irish woodlands (validation process is described in Additional File 2, Text 1). Based on the validation process, results pertaining to European robin ( Erithacus rubecula ), Eurasian wren ( Troglodytes troglodytes ), and common blackbird ( Turdus merula ) were used for further analysis. Environmental factors Local weather conditions (humidity, air temperature and windspeed) at the hour of commencement of each site visit were recorded using an online weather information platform ( www.timeanddate.com ). Datasets from the nearest weather station to each site (maximum distance of 26.1km from site) were obtained from the Irish meteorological service [ 35 ]. Datasets included rainfall, minimum and maximum air temperature, 10cm cut grass temperature, and soil temperature in the locality of the site on the day of the visit, and for the day, week (mean), and month (mean) preceding the visit. The mean minimum air and grass temperatures across the winter period (November to January) preceding site visits was also obtained. The 2018 Corine [ 36 ] landcover dataset and Google Earth [ 37 ] imagery were both used to measure the area of the woodland in which each site was located. The Corine dataset [ 36 ] was also used to define the woodland type and the minimum distance between the woodland and the nearest urban area (i.e. an area of ‘continuous urban fabric’ as defined by the 2018 Corine dataset [ 36 ]. The habitat type of the site in each woodland within which the tick and host data collection took place was also classified using the standard habitat classification system for Ireland [ 38 ]. Finally, the minimum and maximum vegetation height was measured at up to six points along each tick collection transect to calculate a site visit mean for these datapoints. Statistical analyses Data visualisation was carried out using R software version 2022.2.02 [ 39 ], and statistical analyses were carried out using IBM SPSS software version 29.0.1.0 [ 40 ]. Generalised linear mixed models (GLMMs) were fitted to explore the variables explaining tick presence vs absence, adult tick abundance, and nymphal tick abundance. Data from Year 1 (2020) were used for presence vs absence models as there was no a priori expectation of ticks during this fieldwork season, whereas in seasons thereafter, work was focussed on sites where ticks were present during Year 1. Data from Year 2 (2021) and Year 3 (2022) visits at which ticks were found were used for the abundance models. Before models were built, we first used data visualisation to remove host variables with relationships pertaining to one site only. We then ran an exploratory Pearson’s correlation to rank the applicability of all potential independent variables based on their correlation with the dependent variable. All independent variables were then assessed for collinearity using pairwise Pearson's correlations, and where a statistically significant correlation was found between any two variables, the lower ranking (i.e. less relevant) variable was removed from any further analysis. GLMMs with a binomial regression were fitted for the presence vs absence models, and with a negative binomial regression to account for overdispersion for the abundance models. In all cases, study site used as a random effect to account for clustering caused by repeat sampling at each site. Separate models were fitted to explore the significance of weather, habitat, and host factors on nymphal abundance, before fitting a global model which incorporated the fixed effects of the best-fit models from all three previous selection processes. In all cases, model selection was undertaken by first fitting a full model and using backward stepwise elimination based on the corrected Akaike information criterion (AICc). To prevent overfitting, the best-fit model was selected only after a ratio of 1:10 of fixed effects: observations had been reached [ 41 ]. Finally, as the impact of hosts on nymphal abundance was of particular interest in the current study, models were built to explore each host parameter separately as predictors of nymphal abundance. Results A total of 1,931 I. ricinus (226 and 1705 adults and nymphs respectively) were collected over 126 visits across 15 sites during the years 2020, 2021 and 2022 (Table 2 ). While all sites were sampled on at least six occasions, ticks were not found during any visit to almost half (47%) of the sites (Table 2 ), and ticks were repeatedly found over the three study years at six sites, all of which were in the west of Ireland. Derroura, Co. Galway had the greatest abundance of ticks (103 adults, 569 nymphs) accounting for almost 35% of ticks found in this study (Fig. 1 ; Table 2 ; Additional File 1, Table S3). Nymphal abundance across all sites was greatest in April (Fig. 2 ). The mean number of birdsong events per 15-minute sampling period across all 15 sites was greatest for European robin (2.38) followed by Eurasian wren (1.10) and blackbird (0.03) (Additional File 1, Table S3). Mean mammal activity events per trap night ranged from 0.06 (Merlin Wood) to 3.44 (Phoenix Park), and peaked in August (1.28 events per camera trap night, Fig. 2 ). Deer were recorded at 8 (> 50%) of the 15 sites, and deer presence at site level was associated with greater numbers of nymphal ticks (T = 3174, p < 001, Mann-Whitney U). The results of the best-fit GLMMs describing tick presence vs absence, adult tick abundance and nymphal abundance are detailed below. Table 2 – Adult / nymphal I. ricinus abundance, mammal species, and mammal activity events from Years 1–3 (2020–2022) Site Adult abundance per visit a Nymph abundance per visit a Number of mammal species recorded per trap night b Mammal activity events recorded per trap night c Barna Total 0 0 0.58 4.33 Dc, V = 6, TN = 36 Mean 0 0 0.15 1.08 SD 0 0 0.04 1.20 Crone Wood Total 0 1 0.83 3.08 Cf, V = 6, TN = 42 Mean 0 0.17 0.21 0.77 SD 0 0.41 0.11 0.19 Deerpark Total 0 0 0.58 2.58 Dc, V = 6, TN = 36 Mean 0 0 0.15 0.65 SD 0 0 0.11 1.07 Derroura Total 103 569 0.83 1.33 Mx, V = 12, TN = 78 Mean 8.58 47.42 0.08 0.13 SD 5.57 33.01 0.08 0.15 Derrycrag Total 18 178 2.08 26.08 Dc, V = 12, TN = 78 Mean 1.50 14.83 0.23 2.90 SD 2.24 22.31 0.16 2.69 Glen Of The Downs Total 0 0 1.08 10.00 Dc, V = 6, TN = 30 Mean 0 0 0.27 2.50 SD 0 0 0.09 2.81 Hazelwood Total 0 0 0.08 1.92 Dc, V = 6, TN = 42 Mean 0 0 0.02 0.48 SD 0 0 0.04 0.96 Knocknarea Total 1 1 0.50 2.00 Cf, V = 6, TN = 30 Mean 0.17 0.17 0.12 0.50 SD 0.41 0.41 0.16 0.79 Merlin Total 0 0 0 0 Mx, V = 6, TN = 48 Mean 0 0 0.02 0.06 SD 0 0 0.04 0.13 Newvillage Total 30 146 0.50 1.00 Cf, V = 12, TN = 114 Mean 2.50 12.17 0.05 0.10 SD 2.58 13.65 0.06 0.12 Phoenix Park Total 0 0 0.58 10.33 Dc, V = 6, TN = 36 Mean 0 0 0.19 3.44 SD 0 0 0.10 2.97 Portumna Total 25 368 1.08 3.25 Mx, V = 12, TN = 102 Mean 2.08 30.67 0.12 0.36 SD 2.81 35.29 0.08 0.36 Seanaphéistín Total 0 9 0.75 1.58 Cf, V = 12, TN = 102 Mean 0 0.75 0.07 0.16 SD 0 1.14 0.11 0.28 Slishwood Total 49 433 1.67 8.67 Mx, V = 12, TN = 90 Mean 4.08 36.08 0.19 0.96 SD 4.32 39.24 0.12 0.98 Ticknock Total 0 0 0.67 1.00 Cf, V = 6, TN = 18 Mean 0 0 0.22 0.33 SD 0 0 0.26 0.44 Total Total 226 1705 11.91 77.42 V = 126, TN = 786 Mean 1.77 13.32 0.13 0.85 SD 3.55 25.99 0.12 1.54 V = number of site visits; TN = number of trap nights, Mx = mixed woodland, Dc = deciduous woodland, Cf = coniferous woodland a Tick abundance based on 30 x 5m drags undertaken at each visit b Number of different mammal species captured by camera traps for that visit divided by the number of trap nights that the cameras were deployed for that visit c Mammal activity events for each mammal species per visit divided by the number of trap nights that the cameras were deployed for that visit Site visits undertaken in Year 1 (2020, when there was no a priori expectation of tick presence vs absence) were included in the tick presence vs absence model. The best fit model included site as a random effect, with mean minimum soil temperature for the month preceding the visit and mean minimum vegetation height as fixed effects. This model had a marginal pseudo R 2 of 22.6% and a conditional pseudo R 2 also of 67.3% (Table 3 ), meaning that 22.6% of model variance was explained by the included fixed effects, and 67.3% was explained by a combination of the fixed and random effects. However, none of the fixed effect variables included in the best-fit model reached significance, and the F statistic and p value for the fixed effect for the corrected model were 1.575 and 0.22 respectively, indicating that the model does not explain differences between visits to sites where ticks were present vs where they were absent. Table 3 Best fit global model predicting tick presence vs absence for all 15 sites visited in Year 1–2020 Best-fit model AICc Best-fit model pseudo R2 values Best-fit model fixed effects Coefficient 95% CI lower 95% CI upper P-value 253.53 Marginal = 22.6% Conditional = 67.3% Intercept -11.194 -26.916 4.528 0.158 Mean minimum soil temperature for the month preceding the visit 0.822 -0.183 1.827 0.106 Mean minimum vegetation height -0.054 -0.191 0.082 0.427 The fixed effects included in the best fit model for adult tick abundance that investigated weather, host, and habitat factors are listed in Table 4 . The mean minimum air temperature over the preceding winter period showed a significant inverse relationship with adult tick abundance (p = 0.03, Table 4 ). The marginal and conditional pseudo R 2 s for the model were 27.0% and 30.2%, respectively. Table 4 Best fit global model predicting adult tick abundance Best-fit model AICc Best-fit model pseudo R2 values Best-fit model fixed effects Coefficient 95% CI lower 95% CI upper P-value 68.38 Marginal = 27.0% Conditional = 30.2% Intercept 4.17 2.25 6.07 < 0.001 Soil temperature on the visit day -0.09 -0.20 .03 0.12 Mean minimum daily air temperature for the preceding winter (November to January) -0.61 -1.09 -0.13 0.02 Negative Binomial 0.41 n/a n/a n/a As nymphal abundance is of particular interest in this study, the results of the biotic, habitat, host, and global models predicting nymphal abundance are presented. Models for nymphal abundance used site as a random effect and a negative binomial distribution. The best-fit model for the abiotic variables explaining nymphal abundance at each site visit included: (a) mean minimum daily air temperature for the overwintering period (November-March) before the visit, and (b) the month of the visit. The marginal pseudo-R 2 was 17.6% and the conditional pseudo-R 2 was 55.5%. The only variable to reach statistical significance was the month of the visit (F = 3.907, p = 0.004), with visits during April collecting significantly more ticks than either September or October (September - coefficient = -2.403, p = 0.047; October – coefficient = -1.910, p = 0.001). The best-fit model for habitat variables included only the mean minimum vegetation height (cm) of the drag transect area as a fixed effect. The marginal pseudo-R 2 was 29.7% and the conditional pseudo-R 2 was 30.1%. The F statistic and p-value for the corrected model were 18.052 and < 0.001, respectively. For every unit increase in mean minimum vegetation height, there was a 0.73 decrease in the number of nymphs collected (p < 0.001). For host variables, the best-fit model included mean European robin birdsong events at the site the previous year, pine marten ( Martes martes ) activity events on the visit week, and fox ( Vulpes vulpes ) activity events on the week of the visit as fixed effects. Of these, only robin birdsong events the previous year had a significant effect on the model (F = 14.064 coefficient =-0.210, p < 0.001). The marginal and conditional pseudo-R 2 s were 20.6% and 22.8%, respectively. Host variables were also each entered individually as the sole fixed effect in separate negative binomial models with a log link and site as a random effect. The parameters that had significant effects on their individual models were and mean robin birdsong events the previous year (p = 0.003, coefficient = -0.16), mammal activity events per trap night on the week of the visit (p = 0.04, coefficient = -0.38),and deer activity events per trap night on the week of the visit (p = 0.03, coefficient = -0.40), (Fig. 3 ; Additional File 1, Table S4). Of note, deer activity was responsible for a large proportion of overall mammal activity. When the activity of all mammals other than deer was assessed as a variable, it did not reach significance (Additional File 1, Table S4). The best-fit global model for nymphal abundance according to the AICc included the following as fixed effects: month of visit, fox activity events on the week of the visit, and mean minimum vegetation height. The model had a marginal pseudo R 2 of 46.9% and a conditional pseudo R 2 of 61.6%. The fixed effects relating to month and vegetation height reached statistical significance (see Table 5 ). Table 5 – Best fit global model predicting abundance of nymphs Best-fit model AICc Best-fit model pseudo R2 values Fixed effects Coefficient 95% CI lower 95% CI upper P-value 85.37 Marginal = 46.9% Conditional = 61.6% Intercept 3.74 2.91 4.56 < 0.001 Mean minimum vegetation height -0.07 -0.11 -0.03 0.002 Fox activity events 2.79 -2.47 8.06 0.29 Visit month – May vs April -0.67 -1.62 0.29 0.16 Visit month – June vs April -0.31 -1.29 0.67 0.52 Visit month – July vs April 0.07 -0.93 1.07 0.89 Visit month – August vs April -0.46 -1.61 0.69 -0.42 Visit month – September vs April -1.63 -3.58 -0.32 0.10 Visit month – October vs April -2.30 -3.37 -1.23 < 0.001 Negative Binomial 0.57 n/a n/a n/a Discussion Biotic and abiotic factors associated with adult and nymphal tick presence and abundance, and therefore tick-borne pathogen risk, are complex. Ireland, on the periphery of Europe, with its oceanic climate, fragmented landscape, low woodland cover and relatively low vertebrate richness presents a unique set of environmental conditions to investigate in this context. In this study we identify key factors that predict I. ricinus abundance in Irish woodlands with reference to the potential role played by birds and mammals. The results of this study can contribute to discussions regarding potential future changes to I. ricinus ecology in continental Europe under vertebrate diversity decline and increasing habitat fragmentation. This study of 15 woodland sites (varying in size, type, and distance from urban locations) and their ecology revealed large variations in tick abundance both between study sites and over time. Of note, the sites consistently yielding ticks were all located in the west of Ireland, confirming the pattern of tick presence predicted by Zintl et al. [ 28 ]. Using Year 1 (2020) data from across all 15 sites, models were built to assess factors that predict tick presence vs absence at a site. While the best fit model suggests that a lower minimum vegetation height and a higher minimum soil temperature for the month of the visit predicted tick presence, neither of these variables reached significance. It is interesting to note that using twice the sampling intensity of the standardised Vectornet sampling approach [ 29 ], tick presence was confirmed at one site in Year 1 (Crone Wood) only when the additional sampling was undertaken. The other nine sites confirmed as negative for tick presence in Year 1 using the standard Vectornet approach remained negative after intensified sampling. Furthermore, at two of those sites (Seanaphéistín, Knocknarea), a small number of ticks were found in Year 2 of the study, suggesting that the Vectornet method should be carried out over multiple years. As adult tick abundance is a parameter less commonly reported in the literature, models were also built to investigate what factors may influence adult tick abundance. Of note, all factors included in the best fit model for adult tick abundance were abiotic, suggesting that abiotic variables are more important to adult tick activity in Irish woodlands than the range of hosts present or the habitat type. The best-fit model in this study indicated that the mean minimum temperature over the preceding winter (November-January) was inversely associated with adult tick abundance. Mild winter temperatures, and winter seasons free from spells of extreme cold are known to increase I. ricinus survival rates, but conversely, ground snow cover also increases tick survival in winter [ 18 ], and, given the relatively high winter temperatures present in Ireland, this mechanism may be at play when temperatures drop below freezing at our sites. Our findings confirm that winter temperatures in Ireland are an important determinant of adult tick activity. This has important implications in the context of climate change, wherein changing seasonal temperature patterns in northwestern Europe are likely to cause changes to the questing season I. ricinus [ 42 ]. The only factor included in the best fit abiotic model for nymphal abundance was the month of the visit. We found a statistically significant peak in tick activity in April (the first sampling month of the sampling season) reflecting peak questing activity in other studies in Ireland and Britain [ 11 ] in contrast to the bimodal pattern of activity observed elsewhere in Europe [ 11 ]. This is thought to be due to the mild climate provided by the Gulf Stream [ 11 ]. This is an important finding from a public health perspective, as ‘Lyme Disease Awareness Day’ in Ireland falls on the 1st of May, and efforts to increase awareness of disease risk which are made on this day tend to focus on the summer months [ 43 ]. Since our study found that tick bite risk (based on nymphal abundance) was highest in April, public health communication strategies should be adjusted to reflect this. The best-fit model containing habitat variables as predictors of nymphal abundance contained only one fixed effect i.e. the higher the mean minimum vegetation height, the fewer the number of nymphs collected. This may be due to an accepted limitation of the standardised drag sampling method wherein vegetation height affects the number of ticks reached by the drag blanket [ 8 , 23 , 44 ]. This methodology is nonetheless noted to be useful for the collection of data on tick abundance [ 29 ]. Neither woodland type nor the collection site classification emerged as important predictors of nymphal abundance. This reflects findings in Scottish studies [ 23 , 24 ], while contrasting with other studies undertaken in continental Europe, wherein woodland type does influence nymphal abundance [ 11 ], due to the provision of a humid tick microhabitat by leaf litter [ 16 , 18 ]. We also found that weather data pertaining to ambient humidity and precipitation did not improve the fit of models predicting nymphal abundance. These factors together point to the climate of Ireland being sufficiently moist that the moist microclimatic environment provided by deciduous leaf litter [ 18 ] is less of a necessity for tick survival than it is elsewhere. Across the best fit models pertaining to tick hosts, deer activity events and mean robin birdsong events the previous year emerged as significant fixed effects. All had an inverse effect on nymphal abundance. Previous research which took a similar approach to the present study, and which included the same deer species as our study, has either shown a positive correlation between overall deer density and nymph abundance [ 23 , 45 ], or has shown deer density not to be a significant predictor of nymphal abundance [ 21 , 24 ]. However, it has also previously been noted that at extremes of deer density, the relationship between deer density and nymphal abundance is not linear, and that abundance saturates at moderate deer densities [ 46 ]. While the exact size of the deer population in Ireland is unknown, Ireland has an unsustainably high deer population [ 47 , 48 ], so the relationship between deer and ticks in Ireland is likely to be non-linear. This non-linear relationship fits with our findings. Initial data exploration showed that nymphal ticks were more abundant at sites where deer were present, an expected finding [ 18 ]. However, this finding does not take seasonality or deer abundance into account. Further analysis showed an inverse correlation between deer activity and nymphal abundance. We found this effect to be seasonal; while tick activity peaked in April, as discussed, deer activity in our camera trap areas was low in early summer and peaked in August, perhaps coinciding with movement of deer between breeding and the onset of rutting seasons [ 49 ]. These activity dynamics are reflective of the finding of a study of migratory red deer in Norway, which found that the summer ranges of migratory deer overlapped with areas of lower nymphal abundance [ 50 ]. The fact that our results pertaining to deer are explained by seasonal differences between nymphal questing activity and deer activity within woodland habitats further emphasizes the need to undertake longitudinal analysis when predicting markers of tick abundance [ 51 ]. Also of note is the fact that, in our study, we collected deer activity data using camera traps, rather than using density data using faecal transects [ 24 ] or the collection of ticks either side of a deer fence [ 45 ], as seen in the other studies correlating deer with tick abundance in northwestern Europe. Our methodology allowed us to add nuance to our dataset by tracking deer activity across time. Deer activity made up the majority of mammal activity across our sites, and thus the further significant finding pertaining to mammal activity at our sites is also highly influenced by deer activity. This is confirmed by the fact that, when deer activity was removed from total mammal activity, mammal activity events per trap night no longer had a significant effect on nymphal abundance outcomes. We found that increased European robin birdsong events, a ground foraging passerine, was associated with decreased nymphal abundance at collection sites the following study year. This finding further confirms the important relationship between I. ricinus dynamics and passerine birds in northwestern Europe [ 14 , 52 ]. A 2017 study in Scotland assessed the relationship between I. ricinus and various birds (as hosts) that overwinter in Scotland [ 14 ]. The study found that the main avian tick hosts were ground feeding birds – including European robin, and that fewer ticks were found on birds which fed higher in the trees. Interestingly, though nymphs were found to parasitise the birds in the Scottish study, no adult ticks were reported on these birds [ 14 ]. If adult ticks do not use passerine birds as hosts, the number of birds in an area would be unlikely to impact upon adult tick mating success and therefore would not increase larval and nymphal tick abundance in subsequent years. The relationship we noted between robin and nymphal abundance was an inverse effect. This is perhaps explained by the fact that passerine bird diets include arthropods, and previous studies have observed that for other arthropods higher bird activity levels decrease arthropod abundance [53]. Therefore, a predation effect by European robin on adult or larval ticks may explain the inverse relationship between European robin activity and tick abundance the following year. While this study was novel in collecting data on a range of potential bird and mammal hosts alongside longitudinal nymphal abundance data, future research is now needed to confirm the reasons we have postulated for the important relationships we have found between passerine birds (European robin), deer, abiotic factors, and the ecology of I. ricinus in northwestern Europe. This could be achieved by combining a national tick survey with the use of host survey methods including bird mist netting and mammal live trapping, providing data on host abundance and on-host tick counts. Such studies must also incorporate longitudinal analysis undertaken across multiple study years [ 24 ] when studying environmental factors, particularly host factors. Conclusion This study carried out extensive investigations across 15 sampling sites to identify the environmental and host factors associated with questing I. ricinus abundance in Irish woodlands. Our results confirm that tick bite risk is highest in April, and the timing of current public health communication in Ireland should be altered to reflect this finding. This study constitutes the first time that bird data and nymphal abundance data have been assessed together in Ireland, with our results indicating that passerine birds appear to play an important role in the ecology of I. ricinus . Future studies should therefore further examine the relationship and ecological interactions between passerine bird species and I. ricinus . Our results also highlight the importance of measuring markers of host and nymphal abundances over time, as we have found that seasonality is an important factor determining the dynamics between deer activity and nymphal abundance in Ireland. At a broader international level, by combining the assessment of multiple host species along with tick abundance, our data collection methodology can also inform the design of future studies predicting tick abundance and potential bite risk. Such studies could pave the way for further larger-scale, transdisciplinary investigations incorporating quantitative tick drag sampling and non-invasive host monitoring, along with contemporaneous live trapping and tick collection from a broad range of hosts, including birds and mammals. Ideally, studies of this nature would make comparisons between sites in Ireland, where there are limited mammal species available as tick hosts, with more naturally species rich woodland sites on continental Europe. Declarations The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest. Ethical Approval Not applicable Funding The PhD candidateship of which this investigation is a part was funded by The School of Natural Sciences Scholarship, University of Galway (2020–2021) and the Irish Research Council Government of Ireland Scholarship (2020–2022), and academic fees-only funding has also been provided by the Atlantic Technological University, Sligo (2023–2024). Author Contribution R.W. – Conceptualisation, Data collection, Data curation, Formal analysis, Funding Acquisition, Investigation, Methodology, Project Administration, WritingM.G. – Conceptualisation, Methodology, Funding Acquisition, SupervisionC.W. - Methodology, Formal Analysis, SupervisionO.H. - Data collection, Data curation, Formal analysisB.C. - Data collection, Data curation, Formal analysisC.C. - Conceptualisation, Methodology, Funding Acquisition, Supervision Acknowledgement The authors would like to extend their gratitude to the following individuals who assisted with fieldwork data collection: Nessa Lee, Donall Folan, David Walsh, Mícheál McHugh Jewell, Liam Sheil, Elouan Treguier, Lara Marcolin, Dan Walsh, Amie Flattery, Michael Walsh, Orla Walsh, and Iseult Cummins. We offer further thanks to Donall Folan, David Walsh, Mícheál McHugh Jewell, Liam Sheil, and Elouan Treguier for their work on tick identification and processing of mammal trap footage. The authors are grateful to Síofra Sealy for her contribution in providing ornithology expertise, and to Prof. Anetta Zintl for her expertise on ticks in Ireland. Finally, thank you to the National Parks and Wildlife Service, Coillte, the Office of Public Works, and Galway County Council for allowing access to their woodland sites. Data Availability Data is available upon request References Vacek Z, Cukor J, Vacek S, Václavík T, Kybicová K, Bartoška J, et al. Effect of forest structures and tree species composition on common tick ( Ixodes ricinus ) abundance—Case study from Czechia. For Ecol Manag. 2023;529:120676. Dagostin F, Tagliapietra V, Marini G, Ferrari G, Cervellini M, Wint W, et al. High habitat richness reduces the risk of tick-borne encephalitis in Europe: A multi-scale study. One Health. 2024; doi:10.1016/j.onehlt.2023.100669. Hofmeester TR, Jansen PA, Wijnen HJ, Coipan EC, Fonville M, Prins HHT, et al. Cascading effects of predator activity on tick-borne disease risk. Proc Biol Sci. 2017; doi:10.1098/rspb.2017.0453. Zintl A, Moutailler S, Stuart P, Paredis L, Dutraive J, Gonzalez E, et al. Ticks and Tick-borne diseases in Ireland. Ir Vet J. 2017; doi:10.1186/s13620-017-0084-y. Foldvári G. Life cycle and ecology of Ixodes ricinus : the roots of public health importance. In: Braks M, van Wieren S, W T, H S, editors. Ecology and prevention of Lyme borreliosis . Ecology and control of vector-borne diseases. 4: Netherlands: Wageningen Academic Publishers; 2016. p.31-40. ECDC: Ixodes ricinus - Factsheet for experts. https://ecdc.europa.eu/en/disease-vectors/facts/tick-factsheets/ixodes-ricinus (2014). Accessed 16 Jul 2024. Köhler CF, Holding ML, Sprong H, Jansen PA, Esser HJ. Biodiversity in the Lyme-light: ecological restoration and tick-borne diseases in Europe. Trends Parasitol. 2023;39(5):373-85. Janzén T, Hammer M, Petersson M, Dinnétz P. Factors responsible for Ixodes ricinus presence and abundance across a natural-urban gradient. PLoS One. 2023; doi:10.1371/journal.pone.0285841. Hansford KM, Wheeler BW, Tschirren B, Medlock JM. Questing Ixodes ricinus ticks and Borrelia spp . in urban green space across Europe: A review. Zoonoses Public Health. 2022; doi:10.1111/zph.12913. Brugger K, Walter M, Chitimia-Dobler L, Dobler G, Rubel F. Seasonal cycles of the TBE and Lyme borreliosis vector Ixodes ricinus modelled with time-lagged and interval-averaged predictors. Exp Appl Acarol. 2017; doi:10.1007/s10493-017-0197-8. Pfäffle M, Littwin N, Muders SV, Petney TN. The ecology of tick-borne diseases. Int J Parasitol. 2013;43(12-13):1059-77. Daniel M, Malý M, Danielová V, Kříž B, Nuttall P. Abiotic predictors and annual seasonal dynamics of Ixodes ricinus , the major disease vector of Central Europe. Parasit Vectors. 2015; doi:10.1186/s13071-015-1092-y. James MC, Bowman AS, Forbes KJ, Lewis F, McLeod JE, Gilbert L. Environmental determinants of Ixodes ricinus ticks and the incidence of Borrelia burgdorferi sensu lato , the agent of Lyme borreliosis, in Scotland. Parasitology. 2013; 140(2):237-46. Furness RW, Furness EN. Ixodes ricinus parasitism of birds increases at higher winter temperatures. J Vector Ecol. 2018; doi:10.1111/jvec.12283. Gilbert L, Aungier J, Tomkins JL. Climate of origin affects tick ( Ixodes ricinus ) host-seeking behavior in response to temperature: implications for resilience to climate change? Ecol Evol. 2014; doi:10.1002/ece3.1014. Goldstein V, Boulanger N, Schwartz D, George JC, Ertlen D, Zilliox L, et al. Factors responsible for Ixodes ricinus nymph abundance: Are soil features indicators of tick abundance in a French region where Lyme borreliosis is endemic? Ticks Tick Borne Dis. 2018;9(1877-9603 (Electronic)):938-44. Bourdin A, Dokhelar T, Bord S, van Halder I, Stemmelen A, Scherer-Lorenzen M, et al. Forests harbor more ticks than other habitats: A meta-analysis. For Ecol Manag. 2023;541:121081. Kahl O, Gray JS. The biology of Ixodes ricinus with emphasis on its ecology Ticks Tick Borne Dis. 2023;14(2):102114. Gray JS, Kahl O, Robertson JN, Daniel M, Estrada-Peña A, Gettinby G, et al. Lyme borreliosis habitat assessment. Zentralbl Bakteriol.1998;287(3):211-28. Humair PF, Rais O, Gern L. Transmission of Borrelia afzelii from Apodemus mice and Clethrionomys voles to Ixodes ricinus ticks: differential transmission pattern and overwintering maintenance. Parasitology.1999;188(Pt1):33-42. Hofmeester TR, Sprong H, Jansen PA, Prins HHT, van Wieren SE. Deer presence rather than abundance determines the population density of the sheep tick, Ixodes ricinus , in Dutch forests. Parasit Vectors. 2017; doi:10.1186/s13071-017-2370-7. Gilbert L. How landscapes shape Lyme borreliosis risk. In: Braks M, van Wieren S, Takken W, Sprong H, editors. Ecology and Prevention of Lyme borreliosis. Ecology and Control of Vector Borne Diseases. 4: Wageningen Academic Publishers; 2016. Gandy S, Kilbride E, Biek R, Millins C, Gilbert L. No net effect of host density on tick-borne disease hazard due to opposing roles of vector amplification and pathogen dilution. Ecol Evol. 2022; doi:10.1002/ece3.9253. Millins C, Gilbert L, Johnson P, James M, Kilbride E, Birtles R, et al. Heterogeneity in the abundance and distribution of Ixodes ricinus and Borrelia burgdorferi (sensu lato) in Scotland: implications for risk prediction. Parasit Vectors. 2016; doi:10.1186/s13071-016-1875-9. Copernicus: CORINE land cover. https://land.copernicus.eu/pan-european/corine-land-cover. (2018, 2020). Accessed 16 Jul 2024. Baquero R, Telleria J. Species richness, rarity and endemicity of European mammals: a biogeographical approach. Biodiversity Conserv. 2001;10:29-44. Kirstein F, Rijpkema S, Molkenboer M, Gray JS. Local variations in the distribution and prevalence of Borrelia burgdorferi sensu lato genomospecies in Ixodes ricinus ticks. Appl Environ Microbiol. 1997; doi:10.1128/aem.63.3.1102-1106.1997. Zintl A, Zaid T, McKiernan F, Naranjo-Lucena A, Gray J, Brosnan S, et al. Update on the presence of Ixodes ricinus at the western limit of its range and the prevalence of Borrelia burgdorferi sensu lato. Ticks Tick Borne Dis. 2020; doi:10.1016/j.ttbdis.2020.101518. European Centre for Disease Prevention and Control; European Food Safety Authority: Field sampling methods for mosquitoes, sandflies, biting midges and ticks – VectorNet project 2014–2018. https://www.ecdc.europa.eu/sites/default/files/documents/Vector-sampling-field-protocol-2018.pdf. (2018) Accessed 16 Jul 2024. Wearn O, Glover-Kapfer P. Camera Trapping for Conservation; A Guide to Best Practices. WWF-UK; 2017. Cusack JJ, Dickman AJ, Rowcliffe JM, Carbone C, Macdonald DW, Coulson T. Random versus Game Trail-Based Camera Trap Placement Strategy for Monitoring Terrestrial Mammal Communities. PLoS One. 2015; doi:10.1371/journal.pone.0126373. Kahl S, Wood C, Eibl M, Klinck H. BirdNET: A deep learning solution for avian diversity monitoring. Ecol Inform. 2021; doi:10.1016/j.ecoinf.2021.101236. Funosas D, Barbaro L, Schillé L, Elger A, Castagneyrol B, Cauchoix M. Assessing the potential of BirdNET to infer European bird communities from large-scale ecoacoustic data. Ecol Indic. 2024; doi:10.1016/j.ecolind.2024.112146 MetÉireann: Historical Data. https://www.met.ie/climate/available-data/historical-data (2024). Accessed 16 Jul 2024. EPA: Corine Landcover 2018-National (ITM). http://gis.epa.ie/GetData/Download (2018). Accessed 16 Jul 2024. Google Earth. Google Earth Web 2024. https://earth.google.com/web/@54.24054277,-8.38232033,114.44432255a,4352.396781d,35y,0.00000001h,13.17083798t,0r. Accessed 16 Jul 2024. Fossitt J. A guide to habitats in Ireland. Ireland: Heritage Council; 2000. R Core Team. R: A language and environment for statistical computing. R Foundation for Statistical Computing. Vienna Austria; 2019. IBM Corp. SPSS Statistics for Windows. Version 29.0. Armonk, NY: IBM; 2023. Babyak MA. What you see may not be what you get: a brief, nontechnical introduction to overfitting in regression-type models. Psychosom Med. 2004; 66(3): 411-21. Voyiatzaki C, Papailia SI, Venetikou MS, Pouris J, Tsoumani ME, Papageorgiou EG. Climate Changes Exacerbate the Spread of Ixodes ricinus and the Occurrence of Lyme Borreliosis and Tick-Borne Encephalitis in Europe-How Climate Models Are Used as a Risk Assessment Approach for Tick-Borne Diseases. Int J Environ Res Public Health. 2022;19(11). HSE: HSE HPSC advises - Be tick aware, keep you and your family safe from Lyme disease: https://about.hse.ie/news/hse-hpsc-advises-be-tick-aware-keep-you-and-your-family-safe-from-lyme-disease/ (2024). Accesed 16 Jul 2024. Ribeiro R, Eze J, Gunn G, Gilbert L, Macrae A, Auty H. Assessing the spatial distribution of Ixodes ricinus in Scotland using a Bayesian approach. Poster session presented at Vector-Borne Diseases UK (VBD2018). Norwich. Gray JS, Kahl O, Janetzki C, Stein J. Studies on the ecology of Lyme disease in a deer forest in County Galway, Ireland. J Med Entomol. 1992;29(6):915-20. Kilpatrick AM, Dobson ADM, Levi T, Salkeld DJ, Swei A, Ginsberg HS, et al. Lyme disease ecology in a changing world: consensus, uncertainty and critical gaps for improving control. Philos Trans R Soc B. 2017; doi:10.1098/rstb.2016.0117. Purser P, Wilson F, Carden R. Deer and forestry in Ireland: a review of current status and management requirements. Woodlands of Ireland.2009. https://www.woodlandsofireland.com/wp-content/uploads/DeerStrategy.pdf. Accessed 16 Jul 2024. Carden RF, Carlin CM, Marnell F, McElholm D, Hetherington J, Gammell MP. Distribution and range expansion of deer in Ireland. Mammal Rev. 2011;41(4):313-25. Liu Y, Nieuwenhui M. An analysis of habitat-use patterns of fallow and sika deer based on culling data from two estates in Co. Wicklow. Irish Forestry Journal. 2014;71 Qviller L, Risnes-Olsen N, Bærum KM, Meisingset EL, Loe LE, Ytrehus B, et al. Landscape level variation in tick abundance relative to seasonal migration in red deer. PLoS One. 2013; doi:10.1371/journal.pone.0071299. Cat J, Beugnet F, Hoch T, Jongejan F, Prangé A, Chalvet-Monfray K. Influence of the spatial heterogeneity in tick abundance in the modeling of the seasonal activity of Ixodes ricinus nymphs in Western Europe. Exp Appl Acarol. 2017;71(2):115-30. Wilhelmsson P, Pawełczyk O, Jaenson TGT, Waldenström J, Olsen B, Forsberg P, et al. Three Babesia species in Ixodes ricinus ticks from migratory birds in Sweden. Parasit Vectors. 2021; doi:10.1186/s13071-021-04684-8. Heyman E, Gunnarsson B. Management effect on bird and arthropod interaction in suburban woodlands. BMC Ecol. 2011; doi:10.1186/1472-6785-11-8. Additional Declarations No competing interests reported. Supplementary Files GraphicalAbstract.png AdditionaltablesSuppl1.docx BirdsongrecordingmethodsSuppl2.docx Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {\"props\":{\"pageProps\":{\"initialData\":{\"identity\":\"rs-4848879\",\"acceptedTermsAndConditions\":true,\"allowDirectSubmit\":true,\"archivedVersions\":[],\"articleType\":\"Research Article\",\"associatedPublications\":[],\"authors\":[{\"id\":346967388,\"identity\":\"0850e856-95f4-459e-a8ac-706d04a8f3d2\",\"order_by\":0,\"name\":\"Ríona Walsh\",\"email\":\"\",\"orcid\":\"\",\"institution\":\"University of Galway\",\"correspondingAuthor\":false,\"prefix\":\"\",\"firstName\":\"Ríona\",\"middleName\":\"\",\"lastName\":\"Walsh\",\"suffix\":\"\"},{\"id\":346967389,\"identity\":\"1e72f046-b867-4de5-a3c3-661bc13c3bd1\",\"order_by\":1,\"name\":\"Mike Gormally\",\"email\":\"\",\"orcid\":\"\",\"institution\":\"Atlantic Technological University\",\"correspondingAuthor\":false,\"prefix\":\"\",\"firstName\":\"Mike\",\"middleName\":\"\",\"lastName\":\"Gormally\",\"suffix\":\"\"},{\"id\":346967390,\"identity\":\"d6144c65-939c-4862-8e23-4fee0b804e16\",\"order_by\":2,\"name\":\"Christopher Williams\",\"email\":\"\",\"orcid\":\"\",\"institution\":\"Liverpool John Moores University\",\"correspondingAuthor\":false,\"prefix\":\"\",\"firstName\":\"Christopher\",\"middleName\":\"\",\"lastName\":\"Williams\",\"suffix\":\"\"},{\"id\":346967391,\"identity\":\"5f9f6a24-282d-48a8-9e77-ed6b61864d1e\",\"order_by\":3,\"name\":\"Orla Hamilton\",\"email\":\"\",\"orcid\":\"\",\"institution\":\"University of Galway\",\"correspondingAuthor\":false,\"prefix\":\"\",\"firstName\":\"Orla\",\"middleName\":\"\",\"lastName\":\"Hamilton\",\"suffix\":\"\"},{\"id\":346967392,\"identity\":\"cc0316a2-5059-4b60-8efd-a08139a97d8f\",\"order_by\":4,\"name\":\"Belle Carbeck\",\"email\":\"\",\"orcid\":\"\",\"institution\":\"University of Galway\",\"correspondingAuthor\":false,\"prefix\":\"\",\"firstName\":\"Belle\",\"middleName\":\"\",\"lastName\":\"Carbeck\",\"suffix\":\"\"},{\"id\":346967393,\"identity\":\"5ebba386-2db1-481c-8edf-36759b5ad508\",\"order_by\":5,\"name\":\"Caitríona Carlin\",\"email\":\"data:image/png;base64,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\",\"orcid\":\"\",\"institution\":\"University of Galway\",\"correspondingAuthor\":true,\"prefix\":\"\",\"firstName\":\"Caitríona\",\"middleName\":\"\",\"lastName\":\"Carlin\",\"suffix\":\"\"}],\"badges\":[],\"createdAt\":\"2024-08-02 13:49:27\",\"currentVersionCode\":1,\"declarations\":\"\",\"doi\":\"10.21203/rs.3.rs-4848879/v1\",\"doiUrl\":\"https://doi.org/10.21203/rs.3.rs-4848879/v1\",\"draftVersion\":[],\"editorialEvents\":[],\"editorialNote\":\"\",\"failedWorkflow\":false,\"files\":[{\"id\":64567852,\"identity\":\"062cc464-9de7-4ff1-a5d4-4ec0bd9202ef\",\"added_by\":\"auto\",\"created_at\":\"2024-09-16 00:37:26\",\"extension\":\"png\",\"order_by\":1,\"title\":\"Figure 1\",\"display\":\"\",\"copyAsset\":false,\"role\":\"figure\",\"size\":2647384,\"visible\":true,\"origin\":\"\",\"legend\":\"\\u003cp\\u003eLocation of the 15 fieldwork sites (in orange) selected for the study (between years 2020-2022). 1 = Knocknarea; 2 = Deerpark; 3 = Hazelwood; 4 = Slishwood; 5 = Oldtown Wood Phoenix Park; 6 = Ticnock; 7 = Crone Wood; 8 = Glen of the Downs; 9 = Derroura; 10 = Newvillage; 11 = Seanaphéistín; 12 = Barna Wood; Merlin Wood; 14 = Derrycrag; 15 = Portumna\\u003c/p\\u003e\",\"description\":\"\",\"filename\":\"Figure1.png\",\"url\":\"https://assets-eu.researchsquare.com/files/rs-4848879/v1/91f4b0f22a99b645cb3861d6.png\"},{\"id\":64567850,\"identity\":\"b51bdd73-ccee-4902-a3d1-b40eb05d52c1\",\"added_by\":\"auto\",\"created_at\":\"2024-09-16 00:37:25\",\"extension\":\"png\",\"order_by\":2,\"title\":\"Figure 2\",\"display\":\"\",\"copyAsset\":false,\"role\":\"figure\",\"size\":86125,\"visible\":true,\"origin\":\"\",\"legend\":\"\\u003cp\\u003eMean nymphal and adult abundance, mean deer activity events, and mean overall mammal activity events (all species) per visit for each month studied across (2A) all visits to all 15 study sites and (2B) site visits where ticks were present.\\u003c/p\\u003e\",\"description\":\"\",\"filename\":\"Figure2.png\",\"url\":\"https://assets-eu.researchsquare.com/files/rs-4848879/v1/8d2bd633ab3d4d3abe503d98.png\"},{\"id\":64567851,\"identity\":\"cb0eea66-8835-4f5d-bab3-7b9f73b2db78\",\"added_by\":\"auto\",\"created_at\":\"2024-09-16 00:37:25\",\"extension\":\"png\",\"order_by\":3,\"title\":\"Figure 3\",\"display\":\"\",\"copyAsset\":false,\"role\":\"figure\",\"size\":111418,\"visible\":true,\"origin\":\"\",\"legend\":\"\\u003cp\\u003eVisualisation of (A) Deer activity events and (B) mean robin birdsong events the previous year vs nymphal abundance using a negative binomial model, at all sites where ticks were present. Note that the outlier in plot A is a true outlier, and when removed deer activity events remain statistically significant as predictors of nymphal abundance.\\u003c/p\\u003e\",\"description\":\"\",\"filename\":\"FIgure3.png\",\"url\":\"https://assets-eu.researchsquare.com/files/rs-4848879/v1/8865bb3ea8f4c25badd40838.png\"},{\"id\":64570377,\"identity\":\"407be4a8-4fd4-4f3d-9709-81efbf1f604a\",\"added_by\":\"auto\",\"created_at\":\"2024-09-16 01:01:30\",\"extension\":\"pdf\",\"order_by\":0,\"title\":\"\",\"display\":\"\",\"copyAsset\":false,\"role\":\"manuscript-pdf\",\"size\":4410733,\"visible\":true,\"origin\":\"\",\"legend\":\"\",\"description\":\"\",\"filename\":\"manuscript.pdf\",\"url\":\"https://assets-eu.researchsquare.com/files/rs-4848879/v1/3319cdba-418e-4954-b113-4096439f29a0.pdf\"},{\"id\":64567853,\"identity\":\"3bee2002-7338-44de-9d01-3c4bb537e813\",\"added_by\":\"auto\",\"created_at\":\"2024-09-16 00:37:26\",\"extension\":\"png\",\"order_by\":3,\"title\":\"\",\"display\":\"\",\"copyAsset\":false,\"role\":\"supplement\",\"size\":408250,\"visible\":true,\"origin\":\"\",\"legend\":\"\",\"description\":\"\",\"filename\":\"GraphicalAbstract.png\",\"url\":\"https://assets-eu.researchsquare.com/files/rs-4848879/v1/c1f4dbd9d1cbdd3c093c33b7.png\"},{\"id\":64567855,\"identity\":\"f7ed5bb3-7beb-470d-a4fe-f6d9d05a409e\",\"added_by\":\"auto\",\"created_at\":\"2024-09-16 00:37:26\",\"extension\":\"docx\",\"order_by\":7,\"title\":\"\",\"display\":\"\",\"copyAsset\":false,\"role\":\"supplement\",\"size\":34857,\"visible\":true,\"origin\":\"\",\"legend\":\"\",\"description\":\"\",\"filename\":\"AdditionaltablesSuppl1.docx\",\"url\":\"https://assets-eu.researchsquare.com/files/rs-4848879/v1/2b7b66fba5e35fcc5eb44ce3.docx\"},{\"id\":64569083,\"identity\":\"3189f28f-2cbe-4bef-ba5b-45303f850e93\",\"added_by\":\"auto\",\"created_at\":\"2024-09-16 00:45:26\",\"extension\":\"docx\",\"order_by\":8,\"title\":\"\",\"display\":\"\",\"copyAsset\":false,\"role\":\"supplement\",\"size\":15669,\"visible\":true,\"origin\":\"\",\"legend\":\"\",\"description\":\"\",\"filename\":\"BirdsongrecordingmethodsSuppl2.docx\",\"url\":\"https://assets-eu.researchsquare.com/files/rs-4848879/v1/8c6e411043c7a2833d382ef2.docx\"}],\"financialInterests\":\"No competing interests reported.\",\"formattedTitle\":\"Ixodes ricinus in Ireland: exploring the links between environmental factors, host species activity and tick abundance in an area of Europe with limited potential vertebrate hosts\",\"fulltext\":[{\"header\":\"Background\",\"content\":\"\\u003cp\\u003eTicks, vectors to numerous pathogens, are found across a wide geographical range, and, in Europe, tick abundance, distribution, and tick-borne disease incidence are thought to be increasing [\\u003cspan citationid=\\\"CR1\\\" class=\\\"CitationRef\\\"\\u003e1\\u003c/span\\u003e]. This increase is driven by changes in climate, habitat degradation, and vertebrate host diversity and community composition [\\u003cspan citationid=\\\"CR2\\\" class=\\\"CitationRef\\\"\\u003e2\\u003c/span\\u003e, \\u003cspan citationid=\\\"CR3\\\" class=\\\"CitationRef\\\"\\u003e3\\u003c/span\\u003e]. Of the tick species found in Europe, \\u003cem\\u003eIxodes ricinus\\u003c/em\\u003e is the most significant in terms of human impact as it can transmit \\u003cem\\u003eBorrelia burgdorferi\\u003c/em\\u003e s.l., the bacterial complex associated with Lyme borreliosis [\\u003cspan citationid=\\\"CR4\\\" class=\\\"CitationRef\\\"\\u003e4\\u003c/span\\u003e], and tick-borne encephalitis virus (TBEV) [\\u003cspan citationid=\\\"CR1\\\" class=\\\"CitationRef\\\"\\u003e1\\u003c/span\\u003e]. While \\u003cem\\u003eI. ricinus\\u003c/em\\u003e spends the majority of its life off-host, it requires a blood-meal from a host to moult from one life stage (larva, nymph, adult) to the next [\\u003cspan citationid=\\\"CR5\\\" class=\\\"CitationRef\\\"\\u003e5\\u003c/span\\u003e, \\u003cspan citationid=\\\"CR6\\\" class=\\\"CitationRef\\\"\\u003e6\\u003c/span\\u003e]. As tick bite risk increases with increasing nymphal abundance, understanding the factors impacting nymphal abundance in an area is essential to determine the risk of disease to humans [\\u003cspan citationid=\\\"CR7\\\" class=\\\"CitationRef\\\"\\u003e7\\u003c/span\\u003e, \\u003cspan citationid=\\\"CR8\\\" class=\\\"CitationRef\\\"\\u003e8\\u003c/span\\u003e].\\u003c/p\\u003e \\u003cp\\u003eSeasonality affects nymphal abundance, as \\u003cem\\u003eI. ricinus\\u003c/em\\u003e questing behaviour occurs most commonly during the spring and summer months [\\u003cspan citationid=\\\"CR9\\\" class=\\\"CitationRef\\\"\\u003e9\\u003c/span\\u003e]. As such, a dataset containing tick abundance across multiple time points and multiple sites is useful in assessing drivers of abundances in tick populations while taking seasonality into account [\\u003cspan citationid=\\\"CR10\\\" class=\\\"CitationRef\\\"\\u003e10\\u003c/span\\u003e]. Ticks require a high level of humidity (\\u0026gt; 80%) to survive off-host [\\u003cspan citationid=\\\"CR11\\\" class=\\\"CitationRef\\\"\\u003e11\\u003c/span\\u003e], and therefore dry climatic conditions with lower relative humidity generally reduce the questing activity of \\u003cem\\u003eI. ricinus\\u003c/em\\u003e [\\u003cspan citationid=\\\"CR12\\\" class=\\\"CitationRef\\\"\\u003e12\\u003c/span\\u003e]. However, in Scotland, which has a relatively wet and cool climate, fewer questing nymphs are also found when weather and/or habitat conditions are particularly wet; therefore, wet conditions may also be a limiting factor in terms of activity in some parts of the range of \\u003cem\\u003eI. ricinus\\u003c/em\\u003e [\\u003cspan citationid=\\\"CR13\\\" class=\\\"CitationRef\\\"\\u003e13\\u003c/span\\u003e]. Another important factor in the activity levels and survival of \\u003cem\\u003eI. ricinus\\u003c/em\\u003e is temperature [\\u003cspan citationid=\\\"CR14\\\" class=\\\"CitationRef\\\"\\u003e14\\u003c/span\\u003e]. However, \\u003cem\\u003eI. ricinus\\u003c/em\\u003e in differing regions of Europe have been found to be active at varying temperatures, with ticks from more northern areas of Europe being active at lower temperatures than those in southern regions [\\u003cspan citationid=\\\"CR15\\\" class=\\\"CitationRef\\\"\\u003e15\\u003c/span\\u003e]. Environmental and micro-habitat factors such as soil type [\\u003cspan citationid=\\\"CR16\\\" class=\\\"CitationRef\\\"\\u003e16\\u003c/span\\u003e], habitat type, leaf litter, and vegetation structure also impact nymphal abundance in some areas [\\u003cspan citationid=\\\"CR13\\\" class=\\\"CitationRef\\\"\\u003e13\\u003c/span\\u003e, \\u003cspan citationid=\\\"CR17\\\" class=\\\"CitationRef\\\"\\u003e17\\u003c/span\\u003e, \\u003cspan citationid=\\\"CR18\\\" class=\\\"CitationRef\\\"\\u003e18\\u003c/span\\u003e]. Globally, forests are frequently cited as being the preferred habitat of \\u003cem\\u003eI. ricinus\\u003c/em\\u003e [\\u003cspan citationid=\\\"CR17\\\" class=\\\"CitationRef\\\"\\u003e17\\u003c/span\\u003e], with highest abundance at forest edges [\\u003cspan citationid=\\\"CR1\\\" class=\\\"CitationRef\\\"\\u003e1\\u003c/span\\u003e]. Host availability also plays a major role in nymphal abundance. Birds and small mammals are thought to be important hosts for larvae and nymphs, while sheep (\\u003cem\\u003eOvis aries\\u003c/em\\u003e) and deer (\\u003cem\\u003eCervidae\\u003c/em\\u003e) feed adults and are considered reproduction hosts [\\u003cspan citationid=\\\"CR19\\\" class=\\\"CitationRef\\\"\\u003e19\\u003c/span\\u003e, \\u003cspan citationid=\\\"CR20\\\" class=\\\"CitationRef\\\"\\u003e20\\u003c/span\\u003e]. In European disease systems, rodent density [\\u003cspan citationid=\\\"CR3\\\" class=\\\"CitationRef\\\"\\u003e3\\u003c/span\\u003e], deer density or activity [\\u003cspan citationid=\\\"CR21\\\" class=\\\"CitationRef\\\"\\u003e21\\u003c/span\\u003e, \\u003cspan citationid=\\\"CR22\\\" class=\\\"CitationRef\\\"\\u003e22\\u003c/span\\u003e], and vertebrate predator activity [\\u003cspan citationid=\\\"CR3\\\" class=\\\"CitationRef\\\"\\u003e3\\u003c/span\\u003e] have often been cited as impacting nymphal abundance. However, there are differences between regions as to the main host drivers of nymphal abundance. In the Netherlands, for example, rodent numbers are positively correlated with nymphal abundance [\\u003cspan citationid=\\\"CR3\\\" class=\\\"CitationRef\\\"\\u003e3\\u003c/span\\u003e]. However, in Scotland, the opposite seems to be the case, with a study in Scotland reporting more nymphs in sites with low rodent abundance [\\u003cspan citationid=\\\"CR23\\\" class=\\\"CitationRef\\\"\\u003e23\\u003c/span\\u003e]. There is similarly conflicting evidence in the literature as to the nature of the relationship between deer density and nymphal abundance, with one study in Scotland finding a positive [\\u003cspan citationid=\\\"CR13\\\" class=\\\"CitationRef\\\"\\u003e13\\u003c/span\\u003e] correlation, while another study in Scotland found no correlation between deer density and nymphal abundance [\\u003cspan citationid=\\\"CR24\\\" class=\\\"CitationRef\\\"\\u003e24\\u003c/span\\u003e]. Add a sentence here about what we know regarding adult ticks?\\u003c/p\\u003e \\u003cp\\u003eIreland represents an area within the range of \\u003cem\\u003eI. ricinus\\u003c/em\\u003e that has year-round high humidity, a temperate climate, highly fragmented habitats, low woodland cover [\\u003cspan citationid=\\\"CR25\\\" class=\\\"CitationRef\\\"\\u003e25\\u003c/span\\u003e], and is host-species poor [\\u003cspan citationid=\\\"CR26\\\" class=\\\"CitationRef\\\"\\u003e26\\u003c/span\\u003e]. Previous studies have hypothesised that birds possibly play a more important role as tick hosts in Ireland than elsewhere in Europe [\\u003cspan citationid=\\\"CR27\\\" class=\\\"CitationRef\\\"\\u003e27\\u003c/span\\u003e], potentially due to the lower availability of small mammal hosts [\\u003cspan citationid=\\\"CR27\\\" class=\\\"CitationRef\\\"\\u003e27\\u003c/span\\u003e]. This hypothesis is based on the fact that the most dominant genospecies of \\u003cem\\u003eB. burgdorferi s.l\\u003c/em\\u003e. in Ireland are \\u003cem\\u003eBorrelia garinii\\u003c/em\\u003e and \\u003cem\\u003eBorrelia valaisiana\\u003c/em\\u003e, which are bird-related genospecies [\\u003cspan citationid=\\\"CR27\\\" class=\\\"CitationRef\\\"\\u003e27\\u003c/span\\u003e, \\u003cspan citationid=\\\"CR28\\\" class=\\\"CitationRef\\\"\\u003e28\\u003c/span\\u003e]. The confirmation of this postulation would have important implications for understanding changes in \\u003cem\\u003eI. ricinus\\u003c/em\\u003e ecology in the context of mammal diversity loss in continental Europe. However, to the authors’ knowledge, no previous studies have focussed on the relationships between individual bird species and \\u003cem\\u003eI. ricinus\\u003c/em\\u003e ticks in Ireland. Furthermore, although extensive primary studies assessing nymphal infection prevalence in Ireland have been undertaken [\\u003cspan citationid=\\\"CR28\\\" class=\\\"CitationRef\\\"\\u003e28\\u003c/span\\u003e], and tick-presence is also known to be more likely in the west of Ireland [\\u003cspan citationid=\\\"CR28\\\" class=\\\"CitationRef\\\"\\u003e28\\u003c/span\\u003e], an in-depth analysis combining the effects of abiotic, habitat, and host-related factors on questing nymphal tick abundance, and therefore tick bite risk, has never (to the authors’ knowledge) been undertaken in Ireland. The results of such a study in Ireland will help us to understand the factors affecting tick bite risk in particularly host-depauperate, and highly fragmented areas of the distribution of \\u003cem\\u003eI. ricinus\\u003c/em\\u003e.\\u003c/p\\u003e \\u003cp\\u003eTherefore, the aims of this study are to:\\u003c/p\\u003e \\u003cp\\u003e \\u003c/p\\u003e\\u003cul\\u003e \\u003cli\\u003e \\u003cp\\u003eIdentify key abiotic and biotic factors that predict nymphal/adult \\u003cem\\u003eI. ricinus\\u003c/em\\u003e abundance in woodland sites in Ireland.\\u003c/p\\u003e \\u003c/li\\u003e \\u003cli\\u003e \\u003cp\\u003eAssess the relative role of bird and mammal species as predictors of nymphal tick abundance within a highly fragmented and mammal host-species poor region of Europe.\\u003c/p\\u003e \\u003c/li\\u003e \\u003c/ul\\u003e \"},{\"header\":\"Methods\",\"content\":\"\\u003ch2\\u003eSite selection\\u003c/h2\\u003e\\n\\u003cp\\u003eFifteen sites in the east and west of Ireland, covering a range of woodland types, sizes, and distances from urban centres were selected (Fig. \\u003cspan class=\\\"InternalRef\\\"\\u003e1\\u003c/span\\u003e; Additional file 1, Table \\u003cspan class=\\\"InternalRef\\\"\\u003eS1\\u003c/span\\u003e;). In Year 1 (2020) all sites were sampled for adult and nymphal ticks four times between April and November (season of peak \\u003cem\\u003eI. ricinus\\u003c/em\\u003e activity) and those sites which yielded ticks on multiple visits were subsequently sampled four times in both Years 2 (2021) and 3 (2022). Sites on which no or very few (two or fewer) ticks were found in Year 1 (2020) were sampled twice in Year 2 (2021) only, with the exception of Seanaph\\u0026eacute;ist\\u0026iacute;n, which was re-visited four times in 2021 and 2022 for the purposes of other analyses outside the scope of this study (Table \\u003cspan class=\\\"InternalRef\\\"\\u003e1\\u003c/span\\u003e; Additional File 1, Table \\u003cspan class=\\\"InternalRef\\\"\\u003eS2\\u003c/span\\u003e). Woodland edges were selected for tick sampling at each site since tick abundance at ecotones is thought to be greatest (1) and all repeat visits (tick and host data collection) were at the same locations within each woodland.\\u003c/p\\u003e\\n\\u003cdiv class=\\\"gridtable\\\"\\u003e\\n \\u003ctable id=\\\"Tab1\\\" border=\\\"1\\\"\\u003e\\n \\u003ccaption language=\\\"En\\\"\\u003e\\n \\u003cdiv class=\\\"CaptionNumber\\\"\\u003eTable 1\\u003c/div\\u003e\\n \\u003cdiv class=\\\"CaptionContent\\\"\\u003e\\n \\u003cp\\u003eCharacteristics of all fifteen fieldwork sites, listed in alphabetical order\\u003c/p\\u003e\\n \\u003c/div\\u003e\\n \\u003c/caption\\u003e\\n \\u003cthead\\u003e\\n \\u003ctr\\u003e\\n \\u003cth align=\\\"left\\\"\\u003e\\n \\u003cp\\u003eName\\u003c/p\\u003e\\n \\u003c/th\\u003e\\n \\u003cth align=\\\"left\\\"\\u003e\\n \\u003cp\\u003eType\\u003c/p\\u003e\\n \\u003c/th\\u003e\\n \\u003cth align=\\\"left\\\"\\u003e\\n \\u003cp\\u003eSize (Ha)\\u003csup\\u003e+\\u003c/sup\\u003e\\u003c/p\\u003e\\n \\u003c/th\\u003e\\n \\u003cth align=\\\"left\\\"\\u003e\\n \\u003cp\\u003eDistance to urban fabric (Km)\\u003csup\\u003e+\\u003c/sup\\u003e\\u003c/p\\u003e\\n \\u003c/th\\u003e\\n \\u003cth align=\\\"left\\\"\\u003e\\n \\u003cp\\u003eCounty\\u003c/p\\u003e\\n \\u003c/th\\u003e\\n \\u003c/tr\\u003e\\n \\u003c/thead\\u003e\\n \\u003ctbody\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003eBarna Woods\\u003csup\\u003ea\\u003c/sup\\u003e\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003eDeciduous\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003eWoodland size not given in dataset\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e\\u0026lt;\\u0026thinsp;1\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003eGalway\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003eCrone Wood\\u003csup\\u003ea\\u003c/sup\\u003e\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003eConiferous\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e790\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e6\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003eWicklow\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003eDeerpark\\u003csup\\u003ea\\u003c/sup\\u003e\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003eDeciduous\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e63\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e5\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003eSligo\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003eDerroura Forest\\u003csup\\u003eb\\u003c/sup\\u003e\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003eMixed\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e1257\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e28\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003eGalway\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003eDerrycrag Woods\\u003csup\\u003eb\\u003c/sup\\u003e\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003eDeciduous\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e498\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e38\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003eGalway\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003eGlen of the Downs\\u003csup\\u003ea\\u003c/sup\\u003e\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003eDeciduous\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e488\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e\\u0026lt;\\u0026thinsp;1\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003eWicklow\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003eHazelwood\\u003csup\\u003ea\\u003c/sup\\u003e\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003eDeciduous\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e176\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e\\u0026lt;\\u0026thinsp;1\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003eSligo\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003eKnocknarea\\u003csup\\u003ea\\u003c/sup\\u003e\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003eConiferous\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e90\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e4\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003eSligo\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003eMerlin Woods\\u003csup\\u003ea\\u003c/sup\\u003e\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003eMixed\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e40\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e\\u0026lt;\\u0026thinsp;1\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003eGalway\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003eNewvillage Forest\\u003csup\\u003eb\\u003c/sup\\u003e\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003eConiferous\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e412\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e23\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003eGalway\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003ePhoenix Park Oldtown wood\\u003csup\\u003ea\\u003c/sup\\u003e\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003eDeciduous\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003eWoodland size not given in dataset\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e\\u0026lt;\\u0026thinsp;1\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003eDublin\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003ePortumna\\u003csup\\u003eb\\u003c/sup\\u003e\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003eMixed\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e439\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e41\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003eGalway\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003eSeanaph\\u0026eacute;ist\\u0026iacute;n\\u003csup\\u003eb\\u003c/sup\\u003e\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003eConiferous\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e3366\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e19\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003eGalway\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003eSlishwood\\u003csup\\u003eb\\u003c/sup\\u003e\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003eMixed\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e558\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e5\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003eSligo\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003eTicknock\\u003csup\\u003ea\\u003c/sup\\u003e\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003eConiferous\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e248\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e\\u0026lt;\\u0026thinsp;1\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003eDublin\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003c/tbody\\u003e\\n \\u003ctfoot\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd colspan=\\\"5\\\"\\u003e\\u003csup\\u003e+\\u003c/sup\\u003eas per Corine 2018 dataset\\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd colspan=\\\"5\\\"\\u003e\\u003csup\\u003ea\\u003c/sup\\u003eSampled four times per year in Year 1 (2020) and twice in Year 2 (2021) only\\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd colspan=\\\"5\\\"\\u003e\\u003csup\\u003eb\\u003c/sup\\u003eSampled four times per year in Years 1 (2020), 2 (2021) and 3 (2022)\\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003c/tfoot\\u003e\\n \\u003c/table\\u003e\\n\\u003c/div\\u003e\\n\\u003ch2\\u003eTick collection\\u003c/h2\\u003e\\n\\u003cp\\u003eTick collection utilitsed the standard tick dragging methodology as described by Vectornet [\\u003cspan class=\\\"CitationRef\\\"\\u003e29\\u003c/span\\u003e] for tick abundance measurement. This method involves dragging a 1m\\u003csup\\u003e2\\u003c/sup\\u003e white cotton fabric along a 5m transect, raising it for 5m and then dragging it again for another 5m until a total of 30 drags have been completed. For efficiency, all adults and nymphal ticks were removed from the drag material after every 5th drag and stored in labelled vials containing 70% ethanol at 4\\u0026deg;C. Nymphal and adult tick abundance per visit (i.e. for 30 x 5m drags\\u0026thinsp;=\\u0026thinsp;150m\\u003csup\\u003e2\\u003c/sup\\u003e) was calculated. In accordance with Vectornet recommendations, ticks were considered absent from the study area if no tick was collected after 60 x 5m\\u003csup\\u003e2\\u003c/sup\\u003e drags [\\u003cspan class=\\\"CitationRef\\\"\\u003e29\\u003c/span\\u003e]. All ticks were identified to species level with reference to the key by Arthur [\\u003cspan class=\\\"CitationRef\\\"\\u003e30\\u003c/span\\u003e] using a light microscope (Olympus CX40) at 100-400X.\\u003c/p\\u003e\\n\\u003ch2\\u003eMammal data collection\\u003c/h2\\u003e\\n\\u003cp\\u003eRelative vertebrate host activity was assessed at each site using mammal camera traps and birdsong analysis. Two trail cameras were placed at each site for a duration of six nights at a time that coincided with a tick collection visit. Cameras were placed (0.5m \\u0026ndash; 1m in height [\\u003cspan class=\\\"CitationRef\\\"\\u003e31\\u003c/span\\u003e]), close to the tick collection transect, facing away from any human-made paths. Cameras were set to photograph capture mode to ensure battery was conserved throughout the deployment period. Of the two cameras deployed for each six-night period, one was baited with dry cat food to target small to medium mammals, while the other was pointed towards an animal trail where one was visible [\\u003cspan class=\\\"CitationRef\\\"\\u003e32\\u003c/span\\u003e]. Bait was contained within a metal tea strainer hung from a branch approximately 1.5m high and approximately 2- 4m from the camera [\\u003cspan class=\\\"CitationRef\\\"\\u003e31\\u003c/span\\u003e].\\u003c/p\\u003e\\n\\u003cp\\u003eThe number of replicates of camera trap deployments for each site during each study season can be seen in Additional File 1, Table \\u003cspan class=\\\"InternalRef\\\"\\u003eS2\\u003c/span\\u003e. Relative host activity was calculated based on the number of captures of each vertebrate species per camera trap night, and recorded as \\u0026lsquo;host activity events\\u0026rsquo; [\\u003cspan class=\\\"CitationRef\\\"\\u003e31\\u003c/span\\u003e]. No distinction was made between the deer species captured by camera trap footage as a UK study has demonstrated that different deer species contribute similar effects to tick abundance and infection prevalence [\\u003cspan class=\\\"CitationRef\\\"\\u003e23\\u003c/span\\u003e]. Camera traps were deployed in tandem with tick collection site visits so that analysis of vertebrate host activity could be linked with that of tick abundance data. Mean host activity at the site during the previous study year was also calculated for sites visited in 2021 and 2022, to reflect the possibility that host activity may have a delayed effect on tick numbers, given the extended periods between \\u003cem\\u003eI. ricinus\\u003c/em\\u003e bloodmeals [\\u003cspan class=\\\"CitationRef\\\"\\u003e5\\u003c/span\\u003e].\\u003c/p\\u003e\\n\\u003ch2\\u003eBirdsong Data Collection\\u003c/h2\\u003e\\n\\u003cp\\u003eBirdsong, recorded to determine potential avian hosts of ticks at the woodland sites, was captured via 15-minute ambient bioacoustic recordings taken during tick sampling at each site. The recorder was placed at ground level beside the tick collection transect (see Additional File 1, Table \\u003cspan class=\\\"InternalRef\\\"\\u003eS2\\u003c/span\\u003e for number of replicates of birdsong recordings).\\u003c/p\\u003e\\n\\u003cp\\u003eThe resultant soundscape was analysed using BirdNet (33, 34). BirdNet was validated for use in the setting of Irish woodlands (validation process is described in Additional File 2, Text 1). Based on the validation process, results pertaining to European robin (\\u003cem\\u003eErithacus rubecula\\u003c/em\\u003e), Eurasian wren (\\u003cem\\u003eTroglodytes troglodytes\\u003c/em\\u003e), and common blackbird (\\u003cem\\u003eTurdus merula\\u003c/em\\u003e) were used for further analysis.\\u003c/p\\u003e\\n\\u003ch2\\u003eEnvironmental factors\\u003c/h2\\u003e\\n\\u003cp\\u003eLocal weather conditions (humidity, air temperature and windspeed) at the hour of commencement of each site visit were recorded using an online weather information platform (\\u003cspan class=\\\"ExternalRef\\\"\\u003e\\u003cspan class=\\\"RefSource\\\"\\u003ewww.timeanddate.com\\u003c/span\\u003e\\u003c/span\\u003e). Datasets from the nearest weather station to each site (maximum distance of 26.1km from site) were obtained from the Irish meteorological service [\\u003cspan class=\\\"CitationRef\\\"\\u003e35\\u003c/span\\u003e]. Datasets included rainfall, minimum and maximum air temperature, 10cm cut grass temperature, and soil temperature in the locality of the site on the day of the visit, and for the day, week (mean), and month (mean) preceding the visit. The mean minimum air and grass temperatures across the winter period (November to January) preceding site visits was also obtained.\\u003c/p\\u003e\\n\\u003cp\\u003eThe 2018 Corine [\\u003cspan class=\\\"CitationRef\\\"\\u003e36\\u003c/span\\u003e] landcover dataset and Google Earth [\\u003cspan class=\\\"CitationRef\\\"\\u003e37\\u003c/span\\u003e] imagery were both used to measure the area of the woodland in which each site was located. The Corine dataset [\\u003cspan class=\\\"CitationRef\\\"\\u003e36\\u003c/span\\u003e] was also used to define the woodland type and the minimum distance between the woodland and the nearest urban area (i.e. an area of \\u0026lsquo;continuous urban fabric\\u0026rsquo; as defined by the 2018 Corine dataset [\\u003cspan class=\\\"CitationRef\\\"\\u003e36\\u003c/span\\u003e]. The habitat type of the site in each woodland within which the tick and host data collection took place was also classified using the standard habitat classification system for Ireland [\\u003cspan class=\\\"CitationRef\\\"\\u003e38\\u003c/span\\u003e]. Finally, the minimum and maximum vegetation height was measured at up to six points along each tick collection transect to calculate a site visit mean for these datapoints.\\u003c/p\\u003e\\n\\u003ch2\\u003eStatistical analyses\\u003c/h2\\u003e\\n\\u003cp\\u003eData visualisation was carried out using R software version 2022.2.02 [\\u003cspan class=\\\"CitationRef\\\"\\u003e39\\u003c/span\\u003e], and statistical analyses were carried out using IBM SPSS software version 29.0.1.0 [\\u003cspan class=\\\"CitationRef\\\"\\u003e40\\u003c/span\\u003e].\\u003c/p\\u003e\\n\\u003cp\\u003eGeneralised linear mixed models (GLMMs) were fitted to explore the variables explaining tick presence vs absence, adult tick abundance, and nymphal tick abundance.\\u003c/p\\u003e\\n\\u003cp\\u003eData from Year 1 (2020) were used for presence vs absence models as there was no \\u003cem\\u003ea priori\\u003c/em\\u003e expectation of ticks during this fieldwork season, whereas in seasons thereafter, work was focussed on sites where ticks were present during Year 1. Data from Year 2 (2021) and Year 3 (2022) visits at which ticks were found were used for the abundance models.\\u003c/p\\u003e\\n\\u003cp\\u003eBefore models were built, we first used data visualisation to remove host variables with relationships pertaining to one site only. We then ran an exploratory Pearson\\u0026rsquo;s correlation to rank the applicability of all potential independent variables based on their correlation with the dependent variable. All independent variables were then assessed for collinearity using pairwise Pearson\\u0026apos;s correlations, and where a statistically significant correlation was found between any two variables, the lower ranking (i.e. less relevant) variable was removed from any further analysis. GLMMs with a binomial regression were fitted for the presence vs absence models, and with a negative binomial regression to account for overdispersion for the abundance models. In all cases, study site used as a random effect to account for clustering caused by repeat sampling at each site. Separate models were fitted to explore the significance of weather, habitat, and host factors on nymphal abundance, before fitting a global model which incorporated the fixed effects of the best-fit models from all three previous selection processes. In all cases, model selection was undertaken by first fitting a full model and using backward stepwise elimination based on the corrected Akaike information criterion (AICc). To prevent overfitting, the best-fit model was selected only after a ratio of 1:10 of fixed effects: observations had been reached [\\u003cspan class=\\\"CitationRef\\\"\\u003e41\\u003c/span\\u003e]. Finally, as the impact of hosts on nymphal abundance was of particular interest in the current study, models were built to explore each host parameter separately as predictors of nymphal abundance.\\u003c/p\\u003e\"},{\"header\":\"Results\",\"content\":\"\\u003cp\\u003eA total of 1,931 \\u003cem\\u003eI. ricinus\\u003c/em\\u003e (226 and 1705 adults and nymphs respectively) were collected over 126 visits across 15 sites during the years 2020, 2021 and 2022 (Table\\u0026nbsp;\\u003cspan refid=\\\"Tab2\\\" class=\\\"InternalRef\\\"\\u003e2\\u003c/span\\u003e). While all sites were sampled on at least six occasions, ticks were not found during any visit to almost half (47%) of the sites (Table\\u0026nbsp;\\u003cspan refid=\\\"Tab2\\\" class=\\\"InternalRef\\\"\\u003e2\\u003c/span\\u003e), and ticks were repeatedly found over the three study years at six sites, all of which were in the west of Ireland. Derroura, Co. Galway had the greatest abundance of ticks (103 adults, 569 nymphs) accounting for almost 35% of ticks found in this study (Fig.\\u0026nbsp;\\u003cspan refid=\\\"Fig1\\\" class=\\\"InternalRef\\\"\\u003e1\\u003c/span\\u003e; Table\\u0026nbsp;\\u003cspan refid=\\\"Tab2\\\" class=\\\"InternalRef\\\"\\u003e2\\u003c/span\\u003e; Additional File 1, Table S3). Nymphal abundance across all sites was greatest in April (Fig.\\u0026nbsp;\\u003cspan refid=\\\"Fig2\\\" class=\\\"InternalRef\\\"\\u003e2\\u003c/span\\u003e). The mean number of birdsong events per 15-minute sampling period across all 15 sites was greatest for European robin (2.38) followed by Eurasian wren (1.10) and blackbird (0.03) (Additional File 1, Table S3). Mean mammal activity events per trap night ranged from 0.06 (Merlin Wood) to 3.44 (Phoenix Park), and peaked in August (1.28 events per camera trap night, Fig.\\u0026nbsp;\\u003cspan refid=\\\"Fig2\\\" class=\\\"InternalRef\\\"\\u003e2\\u003c/span\\u003e). Deer were recorded at 8 (\\u0026gt;\\u0026thinsp;50%) of the 15 sites, and deer presence at site level was associated with greater numbers of nymphal ticks (T\\u0026thinsp;=\\u0026thinsp;3174, p\\u0026thinsp;\\u0026lt;\\u0026thinsp;001, Mann-Whitney U). The results of the best-fit GLMMs describing tick presence vs absence, adult tick abundance and nymphal abundance are detailed below.\\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\\u003e\\u0026ndash; Adult / nymphal \\u003cem\\u003eI. ricinus\\u003c/em\\u003e abundance, mammal species, and mammal activity events from Years 1\\u0026ndash;3 (2020\\u0026ndash;2022)\\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 \\u003cthead\\u003e \\u003ctr\\u003e \\u003cth align=\\\"left\\\" colspan=\\\"2\\\" nameend=\\\"c2\\\" namest=\\\"c1\\\"\\u003e \\u003cp\\u003eSite\\u003c/p\\u003e \\u003c/th\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003eAdult abundance per visit\\u003csup\\u003ea\\u003c/sup\\u003e\\u003c/p\\u003e \\u003c/th\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003eNymph abundance per visit\\u003csup\\u003ea\\u003c/sup\\u003e\\u003c/p\\u003e \\u003c/th\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003eNumber of mammal species recorded per trap night\\u003csup\\u003eb\\u003c/sup\\u003e\\u003c/p\\u003e \\u003c/th\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003eMammal activity events recorded per trap night\\u003csup\\u003ec\\u003c/sup\\u003e\\u003c/p\\u003e \\u003c/th\\u003e \\u003c/tr\\u003e \\u003c/thead\\u003e \\u003ctbody\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eBarna\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003eTotal\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e0\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e0\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e0.58\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003e4.33\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\" morerows=\\\"1\\\" rowspan=\\\"2\\\"\\u003e \\u003cp\\u003eDc, V\\u0026thinsp;=\\u0026thinsp;6, TN\\u0026thinsp;=\\u0026thinsp;36\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003eMean\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e0\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e0\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e0.15\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003e1.08\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003eSD\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e0\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e0\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e0.04\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003e1.20\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eCrone Wood\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003eTotal\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e0\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e1\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e0.83\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003e3.08\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\" morerows=\\\"1\\\" rowspan=\\\"2\\\"\\u003e \\u003cp\\u003eCf, V\\u0026thinsp;=\\u0026thinsp;6, TN\\u0026thinsp;=\\u0026thinsp;42\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003eMean\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e0\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e0.17\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e0.21\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003e0.77\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003eSD\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e0\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e0.41\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e0.11\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003e0.19\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eDeerpark\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003eTotal\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e0\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e0\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e0.58\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003e2.58\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\" morerows=\\\"1\\\" rowspan=\\\"2\\\"\\u003e \\u003cp\\u003eDc, V\\u0026thinsp;=\\u0026thinsp;6, TN\\u0026thinsp;=\\u0026thinsp;36\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003eMean\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e0\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e0\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e0.15\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003e0.65\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003eSD\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e0\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e0\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e0.11\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003e1.07\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eDerroura\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003eTotal\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e103\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e569\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e0.83\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003e1.33\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\" morerows=\\\"1\\\" rowspan=\\\"2\\\"\\u003e \\u003cp\\u003eMx, V\\u0026thinsp;=\\u0026thinsp;12, TN\\u0026thinsp;=\\u0026thinsp;78\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003eMean\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e8.58\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e47.42\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e0.08\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003e0.13\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003eSD\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e5.57\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e33.01\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e0.08\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003e0.15\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eDerrycrag\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003eTotal\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e18\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e178\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e2.08\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003e26.08\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\" morerows=\\\"1\\\" rowspan=\\\"2\\\"\\u003e \\u003cp\\u003eDc, V\\u0026thinsp;=\\u0026thinsp;12, TN\\u0026thinsp;=\\u0026thinsp;78\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003eMean\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e1.50\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e14.83\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e0.23\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003e2.90\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003eSD\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e2.24\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e22.31\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e0.16\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003e2.69\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eGlen Of The Downs\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003eTotal\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e0\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e0\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e1.08\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003e10.00\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\" morerows=\\\"1\\\" rowspan=\\\"2\\\"\\u003e \\u003cp\\u003eDc, V\\u0026thinsp;=\\u0026thinsp;6, TN\\u0026thinsp;=\\u0026thinsp;30\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003eMean\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e0\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e0\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e0.27\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003e2.50\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003eSD\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e0\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e0\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e0.09\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003e2.81\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eHazelwood\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003eTotal\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e0\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e0\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e0.08\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003e1.92\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\" morerows=\\\"1\\\" rowspan=\\\"2\\\"\\u003e \\u003cp\\u003eDc, V\\u0026thinsp;=\\u0026thinsp;6, TN\\u0026thinsp;=\\u0026thinsp;42\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003eMean\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e0\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e0\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e0.02\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003e0.48\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003eSD\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e0\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e0\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e0.04\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003e0.96\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eKnocknarea\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003eTotal\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e1\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e1\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e0.50\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003e2.00\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\" morerows=\\\"1\\\" rowspan=\\\"2\\\"\\u003e \\u003cp\\u003eCf, V\\u0026thinsp;=\\u0026thinsp;6, TN\\u0026thinsp;=\\u0026thinsp;30\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003eMean\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e0.17\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e0.17\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e0.12\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003e0.50\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003eSD\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e0.41\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e0.41\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e0.16\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003e0.79\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eMerlin\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003eTotal\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e0\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e0\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e0\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003e0\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\" morerows=\\\"1\\\" rowspan=\\\"2\\\"\\u003e \\u003cp\\u003eMx, V\\u0026thinsp;=\\u0026thinsp;6, TN\\u0026thinsp;=\\u0026thinsp;48\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003eMean\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e0\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e0\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e0.02\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003e0.06\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003eSD\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e0\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e0\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e0.04\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003e0.13\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eNewvillage\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003eTotal\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e30\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e146\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e0.50\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003e1.00\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\" morerows=\\\"1\\\" rowspan=\\\"2\\\"\\u003e \\u003cp\\u003eCf, V\\u0026thinsp;=\\u0026thinsp;12, TN\\u0026thinsp;=\\u0026thinsp;114\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003eMean\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e2.50\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e12.17\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e0.05\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003e0.10\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003eSD\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e2.58\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e13.65\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e0.06\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003e0.12\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003ePhoenix Park\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003eTotal\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e0\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e0\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e0.58\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003e10.33\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\" morerows=\\\"1\\\" rowspan=\\\"2\\\"\\u003e \\u003cp\\u003eDc, V\\u0026thinsp;=\\u0026thinsp;6, TN\\u0026thinsp;=\\u0026thinsp;36\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003eMean\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e0\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e0\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e0.19\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003e3.44\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003eSD\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e0\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e0\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e0.10\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003e2.97\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003ePortumna\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003eTotal\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e25\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e368\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e1.08\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003e3.25\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\" morerows=\\\"1\\\" rowspan=\\\"2\\\"\\u003e \\u003cp\\u003eMx, V\\u0026thinsp;=\\u0026thinsp;12, TN\\u0026thinsp;=\\u0026thinsp;102\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003eMean\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e2.08\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e30.67\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e0.12\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003e0.36\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003eSD\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e2.81\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e35.29\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e0.08\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003e0.36\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eSeanaph\\u0026eacute;ist\\u0026iacute;n\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003eTotal\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e0\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e9\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e0.75\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003e1.58\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\" morerows=\\\"1\\\" rowspan=\\\"2\\\"\\u003e \\u003cp\\u003eCf, V\\u0026thinsp;=\\u0026thinsp;12, TN\\u0026thinsp;=\\u0026thinsp;102\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003eMean\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e0\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e0.75\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e0.07\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003e0.16\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003eSD\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e0\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e1.14\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e0.11\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003e0.28\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eSlishwood\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003eTotal\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e49\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e433\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e1.67\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003e8.67\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\" morerows=\\\"1\\\" rowspan=\\\"2\\\"\\u003e \\u003cp\\u003eMx, V\\u0026thinsp;=\\u0026thinsp;12, TN\\u0026thinsp;=\\u0026thinsp;90\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003eMean\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e4.08\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e36.08\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e0.19\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003e0.96\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003eSD\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e4.32\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e39.24\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e0.12\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003e0.98\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eTicknock\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003eTotal\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e0\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e0\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e0.67\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003e1.00\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\" morerows=\\\"1\\\" rowspan=\\\"2\\\"\\u003e \\u003cp\\u003eCf, V\\u0026thinsp;=\\u0026thinsp;6, TN\\u0026thinsp;=\\u0026thinsp;18\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003eMean\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e0\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e0\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e0.22\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003e0.33\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003eSD\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e0\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e0\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e0.26\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003e0.44\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eTotal\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003eTotal\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e226\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e1705\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e11.91\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003e77.42\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\" morerows=\\\"1\\\" rowspan=\\\"2\\\"\\u003e \\u003cp\\u003eV\\u0026thinsp;=\\u0026thinsp;126, TN\\u0026thinsp;=\\u0026thinsp;786\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003eMean\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e1.77\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e13.32\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e0.13\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003e0.85\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003eSD\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e3.55\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e25.99\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e0.12\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003e1.54\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003c/tbody\\u003e \\u003c/colgroup\\u003e \\u003ctfoot\\u003e \\u003ctr\\u003e\\u003ctd colspan=\\\"6\\\"\\u003eV\\u0026thinsp;=\\u0026thinsp;number of site visits; TN\\u0026thinsp;=\\u0026thinsp;number of trap nights, Mx\\u0026thinsp;=\\u0026thinsp;mixed woodland, Dc\\u0026thinsp;=\\u0026thinsp;deciduous woodland, Cf\\u0026thinsp;=\\u0026thinsp;coniferous woodland\\u003c/td\\u003e\\u003c/tr\\u003e \\u003ctr\\u003e\\u003ctd colspan=\\\"6\\\"\\u003e\\u003csup\\u003ea\\u003c/sup\\u003eTick abundance based on 30 x 5m drags undertaken at each visit\\u003c/td\\u003e\\u003c/tr\\u003e \\u003ctr\\u003e\\u003ctd colspan=\\\"6\\\"\\u003e\\u003csup\\u003eb\\u003c/sup\\u003e Number of different mammal species captured by camera traps for that visit divided by the number of trap nights that the cameras were deployed for that visit\\u003c/td\\u003e\\u003c/tr\\u003e \\u003ctr\\u003e\\u003ctd colspan=\\\"6\\\"\\u003e\\u003csup\\u003ec\\u003c/sup\\u003e Mammal activity events for each mammal species per visit divided by the number of trap nights that the cameras were deployed for that visit\\u003c/td\\u003e\\u003c/tr\\u003e \\u003c/tfoot\\u003e \\u003c/table\\u003e\\u003c/div\\u003e \\u003c/p\\u003e \\u003cp\\u003e \\u003c/p\\u003e \\u003cp\\u003eSite visits undertaken in Year 1 (2020, when there was no \\u003cem\\u003ea priori\\u003c/em\\u003e expectation of tick presence vs absence) were included in the tick presence vs absence model. The best fit model included site as a random effect, with mean minimum soil temperature for the month preceding the visit and mean minimum vegetation height as fixed effects. This model had a marginal pseudo R\\u003csup\\u003e2\\u003c/sup\\u003e of 22.6% and a conditional pseudo R\\u003csup\\u003e2\\u003c/sup\\u003e also of 67.3% (Table\\u0026nbsp;\\u003cspan refid=\\\"Tab3\\\" class=\\\"InternalRef\\\"\\u003e3\\u003c/span\\u003e), meaning that 22.6% of model variance was explained by the included fixed effects, and 67.3% was explained by a combination of the fixed and random effects. However, none of the fixed effect variables included in the best-fit model reached significance, and the F statistic and p value for the fixed effect for the corrected model were 1.575 and 0.22 respectively, indicating that the model does not explain differences between visits to sites where ticks were present vs where they were absent.\\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\\u003eBest fit global model predicting tick presence vs absence for all 15 sites visited in Year 1\\u0026ndash;2020\\u003c/p\\u003e \\u003c/div\\u003e \\u003c/caption\\u003e \\u003ccolgroup cols=\\\"7\\\"\\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=\\\"char\\\" char=\\\".\\\" class=\\\"colspec\\\" colname=\\\"c6\\\" colnum=\\\"6\\\"\\u003e\\u003c/div\\u003e \\u003cdiv align=\\\"char\\\" char=\\\".\\\" class=\\\"colspec\\\" colname=\\\"c7\\\" colnum=\\\"7\\\"\\u003e\\u003c/div\\u003e \\u003cthead\\u003e \\u003ctr\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eBest-fit model AICc\\u003c/p\\u003e \\u003c/th\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003eBest-fit model pseudo R2 values\\u003c/p\\u003e \\u003c/th\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003eBest-fit model fixed effects\\u003c/p\\u003e \\u003c/th\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003eCoefficient\\u003c/p\\u003e \\u003c/th\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e95% CI lower\\u003c/p\\u003e \\u003c/th\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003e95% CI upper\\u003c/p\\u003e \\u003c/th\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c7\\\"\\u003e \\u003cp\\u003eP-value\\u003c/p\\u003e \\u003c/th\\u003e \\u003c/tr\\u003e \\u003c/thead\\u003e \\u003ctbody\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\" morerows=\\\"2\\\" rowspan=\\\"3\\\"\\u003e \\u003cp\\u003e253.53\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\" morerows=\\\"2\\\" rowspan=\\\"3\\\"\\u003e \\u003cp\\u003eMarginal\\u0026thinsp;=\\u0026thinsp;22.6%\\u003c/p\\u003e \\u003cp\\u003eConditional\\u0026thinsp;=\\u0026thinsp;67.3%\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003eIntercept\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e-11.194\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e-26.916\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003e4.528\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c7\\\"\\u003e \\u003cp\\u003e0.158\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003eMean minimum soil temperature for the month preceding the visit\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e0.822\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e-0.183\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003e1.827\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c7\\\"\\u003e \\u003cp\\u003e0.106\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003eMean minimum vegetation height\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e-0.054\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e-0.191\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003e0.082\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c7\\\"\\u003e \\u003cp\\u003e0.427\\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\\u003eThe fixed effects included in the best fit model for adult tick abundance that investigated weather, host, and habitat factors are listed in Table\\u0026nbsp;\\u003cspan refid=\\\"Tab4\\\" class=\\\"InternalRef\\\"\\u003e4\\u003c/span\\u003e. The mean minimum air temperature over the preceding winter period showed a significant inverse relationship with adult tick abundance (p\\u0026thinsp;=\\u0026thinsp;0.03, Table\\u0026nbsp;\\u003cspan refid=\\\"Tab4\\\" class=\\\"InternalRef\\\"\\u003e4\\u003c/span\\u003e). The marginal and conditional pseudo R\\u003csup\\u003e2\\u003c/sup\\u003es for the model were 27.0% and 30.2%, respectively.\\u003c/p\\u003e \\u003cp\\u003e \\u003cdiv class=\\\"gridtable\\\"\\u003e\\u003ctable float=\\\"Yes\\\" id=\\\"Tab4\\\" border=\\\"1\\\"\\u003e \\u003ccaption language=\\\"En\\\"\\u003e \\u003cdiv class=\\\"CaptionNumber\\\"\\u003eTable 4\\u003c/div\\u003e \\u003cdiv class=\\\"CaptionContent\\\"\\u003e \\u003cp\\u003eBest fit global model predicting adult tick abundance\\u003c/p\\u003e \\u003c/div\\u003e \\u003c/caption\\u003e \\u003ccolgroup cols=\\\"7\\\"\\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=\\\"char\\\" char=\\\".\\\" 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 \\u003cdiv align=\\\"char\\\" char=\\\".\\\" class=\\\"colspec\\\" colname=\\\"c7\\\" colnum=\\\"7\\\"\\u003e\\u003c/div\\u003e \\u003cthead\\u003e \\u003ctr\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eBest-fit model AICc\\u003c/p\\u003e \\u003c/th\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003eBest-fit model pseudo R2 values\\u003c/p\\u003e \\u003c/th\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003eBest-fit model fixed effects\\u003c/p\\u003e \\u003c/th\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003eCoefficient\\u003c/p\\u003e \\u003c/th\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e95% CI lower\\u003c/p\\u003e \\u003c/th\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003e95% CI upper\\u003c/p\\u003e \\u003c/th\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c7\\\"\\u003e \\u003cp\\u003eP-value\\u003c/p\\u003e \\u003c/th\\u003e \\u003c/tr\\u003e \\u003c/thead\\u003e \\u003ctbody\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\" morerows=\\\"3\\\" rowspan=\\\"4\\\"\\u003e \\u003cp\\u003e68.38\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\" morerows=\\\"3\\\" rowspan=\\\"4\\\"\\u003e \\u003cp\\u003eMarginal\\u0026thinsp;=\\u0026thinsp;27.0%\\u003c/p\\u003e \\u003cp\\u003eConditional\\u0026thinsp;=\\u0026thinsp;30.2%\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003eIntercept\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e4.17\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e2.25\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003e6.07\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c7\\\"\\u003e \\u003cp\\u003e\\u0026lt;\\u0026thinsp;0.001\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003eSoil temperature on the visit day\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e-0.09\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e-0.20\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003e.03\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c7\\\"\\u003e \\u003cp\\u003e0.12\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003eMean minimum daily air temperature for the preceding winter (November to January)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e-0.61\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e-1.09\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003e-0.13\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c7\\\"\\u003e \\u003cp\\u003e0.02\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003eNegative Binomial\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e0.41\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003en/a\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003en/a\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c7\\\"\\u003e \\u003cp\\u003en/a\\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\\u003eAs nymphal abundance is of particular interest in this study, the results of the biotic, habitat, host, and global models predicting nymphal abundance are presented. Models for nymphal abundance used site as a random effect and a negative binomial distribution. The best-fit model for the abiotic variables explaining nymphal abundance at each site visit included: (a) mean minimum daily air temperature for the overwintering period (November-March) before the visit, and (b) the month of the visit. The marginal pseudo-R\\u003csup\\u003e2\\u003c/sup\\u003e was 17.6% and the conditional pseudo-R\\u003csup\\u003e2\\u003c/sup\\u003e was 55.5%. The only variable to reach statistical significance was the month of the visit (F\\u0026thinsp;=\\u0026thinsp;3.907, p\\u0026thinsp;=\\u0026thinsp;0.004), with visits during April collecting significantly more ticks than either September or October (September - coefficient = -2.403, p\\u0026thinsp;=\\u0026thinsp;0.047; October \\u0026ndash; coefficient = -1.910, p\\u0026thinsp;=\\u0026thinsp;0.001).\\u003c/p\\u003e \\u003cp\\u003eThe best-fit model for habitat variables included only the mean minimum vegetation height (cm) of the drag transect area as a fixed effect. The marginal pseudo-R\\u003csup\\u003e2\\u003c/sup\\u003e was 29.7% and the conditional pseudo-R\\u003csup\\u003e2\\u003c/sup\\u003e was 30.1%. The F statistic and p-value for the corrected model were 18.052 and \\u0026lt;\\u0026thinsp;0.001, respectively. For every unit increase in mean minimum vegetation height, there was a 0.73 decrease in the number of nymphs collected (p\\u0026thinsp;\\u0026lt;\\u0026thinsp;0.001).\\u003c/p\\u003e \\u003cp\\u003eFor host variables, the best-fit model included mean European robin birdsong events at the site the previous year, pine marten (\\u003cem\\u003eMartes martes\\u003c/em\\u003e) activity events on the visit week, and fox (\\u003cem\\u003eVulpes vulpes\\u003c/em\\u003e) activity events on the week of the visit as fixed effects. Of these, only robin birdsong events the previous year had a significant effect on the model (F\\u0026thinsp;=\\u0026thinsp;14.064 coefficient =-0.210, p\\u0026thinsp;\\u0026lt;\\u0026thinsp;0.001). The marginal and conditional pseudo-R\\u003csup\\u003e2\\u003c/sup\\u003es were 20.6% and 22.8%, respectively.\\u003c/p\\u003e \\u003cp\\u003eHost variables were also each entered individually as the sole fixed effect in separate negative binomial models with a log link and site as a random effect. The parameters that had significant effects on their individual models were and mean robin birdsong events the previous year (p\\u0026thinsp;=\\u0026thinsp;0.003, coefficient = -0.16), mammal activity events per trap night on the week of the visit (p\\u0026thinsp;=\\u0026thinsp;0.04, coefficient = -0.38),and deer activity events per trap night on the week of the visit (p\\u0026thinsp;=\\u0026thinsp;0.03, coefficient = -0.40), (Fig.\\u0026nbsp;\\u003cspan refid=\\\"Fig3\\\" class=\\\"InternalRef\\\"\\u003e3\\u003c/span\\u003e; Additional File 1, Table S4). Of note, deer activity was responsible for a large proportion of overall mammal activity. When the activity of all mammals other than deer was assessed as a variable, it did not reach significance (Additional File 1, Table S4).\\u003c/p\\u003e \\u003cp\\u003e \\u003c/p\\u003e \\u003cp\\u003eThe best-fit global model for nymphal abundance according to the AICc included the following as fixed effects: month of visit, fox activity events on the week of the visit, and mean minimum vegetation height. The model had a marginal pseudo R\\u003csup\\u003e2\\u003c/sup\\u003e of 46.9% and a conditional pseudo R\\u003csup\\u003e2\\u003c/sup\\u003e of 61.6%. The fixed effects relating to month and vegetation height reached statistical significance (see Table\\u0026nbsp;\\u003cspan refid=\\\"Tab5\\\" class=\\\"InternalRef\\\"\\u003e5\\u003c/span\\u003e).\\u003c/p\\u003e \\u003cp\\u003e \\u003cdiv class=\\\"gridtable\\\"\\u003e\\u003ctable float=\\\"Yes\\\" id=\\\"Tab5\\\" border=\\\"1\\\"\\u003e \\u003ccaption language=\\\"En\\\"\\u003e \\u003cdiv class=\\\"CaptionNumber\\\"\\u003eTable 5\\u003c/div\\u003e \\u003cdiv class=\\\"CaptionContent\\\"\\u003e \\u003cp\\u003e\\u0026ndash; Best fit global model predicting abundance of nymphs\\u003c/p\\u003e \\u003c/div\\u003e \\u003c/caption\\u003e \\u003ccolgroup cols=\\\"7\\\"\\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=\\\"char\\\" char=\\\".\\\" 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 \\u003cdiv align=\\\"left\\\" class=\\\"colspec\\\" colname=\\\"c7\\\" colnum=\\\"7\\\"\\u003e\\u003c/div\\u003e \\u003cthead\\u003e \\u003ctr\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eBest-fit model AICc\\u003c/p\\u003e \\u003c/th\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003eBest-fit model pseudo R2 values\\u003c/p\\u003e \\u003c/th\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003eFixed effects\\u003c/p\\u003e \\u003c/th\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003eCoefficient\\u003c/p\\u003e \\u003c/th\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e95% CI lower\\u003c/p\\u003e \\u003c/th\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003e95% CI upper\\u003c/p\\u003e \\u003c/th\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c7\\\"\\u003e \\u003cp\\u003eP-value\\u003c/p\\u003e \\u003c/th\\u003e \\u003c/tr\\u003e \\u003c/thead\\u003e \\u003ctbody\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\" morerows=\\\"9\\\" rowspan=\\\"10\\\"\\u003e \\u003cp\\u003e85.37\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\" morerows=\\\"9\\\" rowspan=\\\"10\\\"\\u003e \\u003cp\\u003eMarginal\\u0026thinsp;=\\u0026thinsp;46.9%\\u003c/p\\u003e \\u003cp\\u003eConditional\\u0026thinsp;=\\u0026thinsp;61.6%\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003eIntercept\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e3.74\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e2.91\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003e4.56\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c7\\\"\\u003e \\u003cp\\u003e\\u0026lt;\\u0026thinsp;0.001\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003eMean minimum vegetation height\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e-0.07\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e-0.11\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003e-0.03\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c7\\\"\\u003e \\u003cp\\u003e0.002\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003eFox activity events\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e2.79\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e-2.47\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003e8.06\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c7\\\"\\u003e \\u003cp\\u003e0.29\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003eVisit month \\u0026ndash; May vs April\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e-0.67\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e-1.62\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003e0.29\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c7\\\"\\u003e \\u003cp\\u003e0.16\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003eVisit month \\u0026ndash; June vs April\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e-0.31\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e-1.29\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003e0.67\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c7\\\"\\u003e \\u003cp\\u003e0.52\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003eVisit month \\u0026ndash; July vs April\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e0.07\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e-0.93\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003e1.07\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c7\\\"\\u003e \\u003cp\\u003e0.89\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003eVisit month \\u0026ndash; August vs April\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e-0.46\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e-1.61\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003e0.69\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c7\\\"\\u003e \\u003cp\\u003e-0.42\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003eVisit month \\u0026ndash; September vs April\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e-1.63\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e-3.58\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003e-0.32\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c7\\\"\\u003e \\u003cp\\u003e0.10\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003eVisit month \\u0026ndash; October vs April\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e-2.30\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e-3.37\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003e-1.23\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c7\\\"\\u003e \\u003cp\\u003e\\u0026lt;\\u0026thinsp;0.001\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003eNegative Binomial\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e0.57\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003en/a\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003en/a\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c7\\\"\\u003e \\u003cp\\u003en/a\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003c/tbody\\u003e \\u003c/colgroup\\u003e \\u003c/table\\u003e\\u003c/div\\u003e \\u003c/p\\u003e\"},{\"header\":\"Discussion\",\"content\":\"\\u003cp\\u003eBiotic and abiotic factors associated with adult and nymphal tick presence and abundance, and therefore tick-borne pathogen risk, are complex. Ireland, on the periphery of Europe, with its oceanic climate, fragmented landscape, low woodland cover and relatively low vertebrate richness presents a unique set of environmental conditions to investigate in this context. In this study we identify key factors that predict \\u003cem\\u003eI. ricinus\\u003c/em\\u003e abundance in Irish woodlands with reference to the potential role played by birds and mammals. The results of this study can contribute to discussions regarding potential future changes to \\u003cem\\u003eI. ricinus\\u003c/em\\u003e ecology in continental Europe under vertebrate diversity decline and increasing habitat fragmentation.\\u003c/p\\u003e \\u003cp\\u003eThis study of 15 woodland sites (varying in size, type, and distance from urban locations) and their ecology revealed large variations in tick abundance both between study sites and over time. Of note, the sites consistently yielding ticks were all located in the west of Ireland, confirming the pattern of tick presence predicted by Zintl \\u003cem\\u003eet al.\\u003c/em\\u003e [\\u003cspan citationid=\\\"CR28\\\" class=\\\"CitationRef\\\"\\u003e28\\u003c/span\\u003e].\\u003c/p\\u003e \\u003cp\\u003eUsing Year 1 (2020) data from across all 15 sites, models were built to assess factors that predict tick presence vs absence at a site. While the best fit model suggests that a lower minimum vegetation height and a higher minimum soil temperature for the month of the visit predicted tick presence, neither of these variables reached significance. It is interesting to note that using twice the sampling intensity of the standardised Vectornet sampling approach [\\u003cspan citationid=\\\"CR29\\\" class=\\\"CitationRef\\\"\\u003e29\\u003c/span\\u003e], tick presence was confirmed at one site in Year 1 (Crone Wood) only when the additional sampling was undertaken. The other nine sites confirmed as negative for tick presence in Year 1 using the standard Vectornet approach remained negative after intensified sampling. Furthermore, at two of those sites (Seanaph\\u0026eacute;ist\\u0026iacute;n, Knocknarea), a small number of ticks were found in Year 2 of the study, suggesting that the Vectornet method should be carried out over multiple years.\\u003c/p\\u003e \\u003cp\\u003eAs adult tick abundance is a parameter less commonly reported in the literature, models were also built to investigate what factors may influence adult tick abundance. Of note, all factors included in the best fit model for adult tick abundance were abiotic, suggesting that abiotic variables are more important to adult tick activity in Irish woodlands than the range of hosts present or the habitat type. The best-fit model in this study indicated that the mean minimum temperature over the preceding winter (November-January) was inversely associated with adult tick abundance. Mild winter temperatures, and winter seasons free from spells of extreme cold are known to increase \\u003cem\\u003eI. ricinus\\u003c/em\\u003e survival rates, but conversely, ground snow cover also increases tick survival in winter [\\u003cspan citationid=\\\"CR18\\\" class=\\\"CitationRef\\\"\\u003e18\\u003c/span\\u003e], and, given the relatively high winter temperatures present in Ireland, this mechanism may be at play when temperatures drop below freezing at our sites. Our findings confirm that winter temperatures in Ireland are an important determinant of adult tick activity. This has important implications in the context of climate change, wherein changing seasonal temperature patterns in northwestern Europe are likely to cause changes to the questing season \\u003cem\\u003eI. ricinus\\u003c/em\\u003e [\\u003cspan citationid=\\\"CR42\\\" class=\\\"CitationRef\\\"\\u003e42\\u003c/span\\u003e].\\u003c/p\\u003e \\u003cp\\u003eThe only factor included in the best fit abiotic model for nymphal abundance was the month of the visit. We found a statistically significant peak in tick activity in April (the first sampling month of the sampling season) reflecting peak questing activity in other studies in Ireland and Britain [\\u003cspan citationid=\\\"CR11\\\" class=\\\"CitationRef\\\"\\u003e11\\u003c/span\\u003e] in contrast to the bimodal pattern of activity observed elsewhere in Europe [\\u003cspan citationid=\\\"CR11\\\" class=\\\"CitationRef\\\"\\u003e11\\u003c/span\\u003e]. This is thought to be due to the mild climate provided by the Gulf Stream [\\u003cspan citationid=\\\"CR11\\\" class=\\\"CitationRef\\\"\\u003e11\\u003c/span\\u003e]. This is an important finding from a public health perspective, as \\u0026lsquo;Lyme Disease Awareness Day\\u0026rsquo; in Ireland falls on the 1st of May, and efforts to increase awareness of disease risk which are made on this day tend to focus on the summer months [\\u003cspan citationid=\\\"CR43\\\" class=\\\"CitationRef\\\"\\u003e43\\u003c/span\\u003e]. Since our study found that tick bite risk (based on nymphal abundance) was highest in April, public health communication strategies should be adjusted to reflect this.\\u003c/p\\u003e \\u003cp\\u003eThe best-fit model containing habitat variables as predictors of nymphal abundance contained only one fixed effect i.e. the higher the mean minimum vegetation height, the fewer the number of nymphs collected. This may be due to an accepted limitation of the standardised drag sampling method wherein vegetation height affects the number of ticks reached by the drag blanket [\\u003cspan citationid=\\\"CR8\\\" class=\\\"CitationRef\\\"\\u003e8\\u003c/span\\u003e, \\u003cspan citationid=\\\"CR23\\\" class=\\\"CitationRef\\\"\\u003e23\\u003c/span\\u003e, \\u003cspan citationid=\\\"CR44\\\" class=\\\"CitationRef\\\"\\u003e44\\u003c/span\\u003e]. This methodology is nonetheless noted to be useful for the collection of data on tick abundance [\\u003cspan citationid=\\\"CR29\\\" class=\\\"CitationRef\\\"\\u003e29\\u003c/span\\u003e]. Neither woodland type nor the collection site classification emerged as important predictors of nymphal abundance. This reflects findings in Scottish studies [\\u003cspan citationid=\\\"CR23\\\" class=\\\"CitationRef\\\"\\u003e23\\u003c/span\\u003e, \\u003cspan citationid=\\\"CR24\\\" class=\\\"CitationRef\\\"\\u003e24\\u003c/span\\u003e], while contrasting with other studies undertaken in continental Europe, wherein woodland type does influence nymphal abundance [\\u003cspan citationid=\\\"CR11\\\" class=\\\"CitationRef\\\"\\u003e11\\u003c/span\\u003e], due to the provision of a humid tick microhabitat by leaf litter [\\u003cspan citationid=\\\"CR16\\\" class=\\\"CitationRef\\\"\\u003e16\\u003c/span\\u003e, \\u003cspan citationid=\\\"CR18\\\" class=\\\"CitationRef\\\"\\u003e18\\u003c/span\\u003e]. We also found that weather data pertaining to ambient humidity and precipitation did not improve the fit of models predicting nymphal abundance. These factors together point to the climate of Ireland being sufficiently moist that the moist microclimatic environment provided by deciduous leaf litter [\\u003cspan citationid=\\\"CR18\\\" class=\\\"CitationRef\\\"\\u003e18\\u003c/span\\u003e] is less of a necessity for tick survival than it is elsewhere.\\u003c/p\\u003e \\u003cp\\u003eAcross the best fit models pertaining to tick hosts, deer activity events and mean robin birdsong events the previous year emerged as significant fixed effects. All had an inverse effect on nymphal abundance. Previous research which took a similar approach to the present study, and which included the same deer species as our study, has either shown a positive correlation between overall deer density and nymph abundance [\\u003cspan citationid=\\\"CR23\\\" class=\\\"CitationRef\\\"\\u003e23\\u003c/span\\u003e, \\u003cspan citationid=\\\"CR45\\\" class=\\\"CitationRef\\\"\\u003e45\\u003c/span\\u003e], or has shown deer density not to be a significant predictor of nymphal abundance [\\u003cspan citationid=\\\"CR21\\\" class=\\\"CitationRef\\\"\\u003e21\\u003c/span\\u003e, \\u003cspan citationid=\\\"CR24\\\" class=\\\"CitationRef\\\"\\u003e24\\u003c/span\\u003e]. However, it has also previously been noted that at extremes of deer density, the relationship between deer density and nymphal abundance is not linear, and that abundance saturates at moderate deer densities [\\u003cspan citationid=\\\"CR46\\\" class=\\\"CitationRef\\\"\\u003e46\\u003c/span\\u003e]. While the exact size of the deer population in Ireland is unknown, Ireland has an unsustainably high deer population [\\u003cspan citationid=\\\"CR47\\\" class=\\\"CitationRef\\\"\\u003e47\\u003c/span\\u003e, \\u003cspan citationid=\\\"CR48\\\" class=\\\"CitationRef\\\"\\u003e48\\u003c/span\\u003e], so the relationship between deer and ticks in Ireland is likely to be non-linear. This non-linear relationship fits with our findings. Initial data exploration showed that nymphal ticks were more abundant at sites where deer were present, an expected finding [\\u003cspan citationid=\\\"CR18\\\" class=\\\"CitationRef\\\"\\u003e18\\u003c/span\\u003e]. However, this finding does not take seasonality or deer abundance into account. Further analysis showed an inverse correlation between deer activity and nymphal abundance. We found this effect to be seasonal; while tick activity peaked in April, as discussed, deer activity in our camera trap areas was low in early summer and peaked in August, perhaps coinciding with movement of deer between breeding and the onset of rutting seasons [\\u003cspan citationid=\\\"CR49\\\" class=\\\"CitationRef\\\"\\u003e49\\u003c/span\\u003e]. These activity dynamics are reflective of the finding of a study of migratory red deer in Norway, which found that the summer ranges of migratory deer overlapped with areas of lower nymphal abundance [\\u003cspan citationid=\\\"CR50\\\" class=\\\"CitationRef\\\"\\u003e50\\u003c/span\\u003e]. The fact that our results pertaining to deer are explained by seasonal differences between nymphal questing activity and deer activity within woodland habitats further emphasizes the need to undertake longitudinal analysis when predicting markers of tick abundance [\\u003cspan citationid=\\\"CR51\\\" class=\\\"CitationRef\\\"\\u003e51\\u003c/span\\u003e]. Also of note is the fact that, in our study, we collected deer activity data using camera traps, rather than using density data using faecal transects [\\u003cspan citationid=\\\"CR24\\\" class=\\\"CitationRef\\\"\\u003e24\\u003c/span\\u003e] or the collection of ticks either side of a deer fence [\\u003cspan citationid=\\\"CR45\\\" class=\\\"CitationRef\\\"\\u003e45\\u003c/span\\u003e], as seen in the other studies correlating deer with tick abundance in northwestern Europe. Our methodology allowed us to add nuance to our dataset by tracking deer activity across time. Deer activity made up the majority of mammal activity across our sites, and thus the further significant finding pertaining to mammal activity at our sites is also highly influenced by deer activity. This is confirmed by the fact that, when deer activity was removed from total mammal activity, mammal activity events per trap night no longer had a significant effect on nymphal abundance outcomes.\\u003c/p\\u003e \\u003cp\\u003eWe found that increased European robin birdsong events, a ground foraging passerine, was associated with decreased nymphal abundance at collection sites the following study year. This finding further confirms the important relationship between \\u003cem\\u003eI. ricinus\\u003c/em\\u003e dynamics and passerine birds in northwestern Europe [\\u003cspan citationid=\\\"CR14\\\" class=\\\"CitationRef\\\"\\u003e14\\u003c/span\\u003e, \\u003cspan citationid=\\\"CR52\\\" class=\\\"CitationRef\\\"\\u003e52\\u003c/span\\u003e]. A 2017 study in Scotland assessed the relationship between \\u003cem\\u003eI. ricinus\\u003c/em\\u003e and various birds (as hosts) that overwinter in Scotland [\\u003cspan citationid=\\\"CR14\\\" class=\\\"CitationRef\\\"\\u003e14\\u003c/span\\u003e]. The study found that the main avian tick hosts were ground feeding birds \\u0026ndash; including European robin, and that fewer ticks were found on birds which fed higher in the trees. Interestingly, though nymphs were found to parasitise the birds in the Scottish study, no adult ticks were reported on these birds [\\u003cspan citationid=\\\"CR14\\\" class=\\\"CitationRef\\\"\\u003e14\\u003c/span\\u003e]. If adult ticks do not use passerine birds as hosts, the number of birds in an area would be unlikely to impact upon adult tick mating success and therefore would not increase larval and nymphal tick abundance in subsequent years. The relationship we noted between robin and nymphal abundance was an inverse effect. This is perhaps explained by the fact that passerine bird diets include arthropods, and previous studies have observed that for other arthropods higher bird activity levels decrease arthropod abundance [53]. Therefore, a predation effect by European robin on adult or larval ticks may explain the inverse relationship between European robin activity and tick abundance the following year.\\u003c/p\\u003e \\u003cp\\u003eWhile this study was novel in collecting data on a range of potential bird and mammal hosts alongside longitudinal nymphal abundance data, future research is now needed to confirm the reasons we have postulated for the important relationships we have found between passerine birds (European robin), deer, abiotic factors, and the ecology of \\u003cem\\u003eI. ricinus\\u003c/em\\u003e in northwestern Europe. This could be achieved by combining a national tick survey with the use of host survey methods including bird mist netting and mammal live trapping, providing data on host abundance and on-host tick counts. Such studies must also incorporate longitudinal analysis undertaken across multiple study years [\\u003cspan citationid=\\\"CR24\\\" class=\\\"CitationRef\\\"\\u003e24\\u003c/span\\u003e] when studying environmental factors, particularly host factors.\\u003c/p\\u003e\"},{\"header\":\"Conclusion\",\"content\":\"\\u003cp\\u003eThis study carried out extensive investigations across 15 sampling sites to identify the environmental and host factors associated with questing \\u003cem\\u003eI. ricinus\\u003c/em\\u003e abundance in Irish woodlands. Our results confirm that tick bite risk is highest in April, and the timing of current public health communication in Ireland should be altered to reflect this finding. This study constitutes the first time that bird data and nymphal abundance data have been assessed together in Ireland, with our results indicating that passerine birds appear to play an important role in the ecology of \\u003cem\\u003eI. ricinus\\u003c/em\\u003e. Future studies should therefore further examine the relationship and ecological interactions between passerine bird species and \\u003cem\\u003eI. ricinus\\u003c/em\\u003e. Our results also highlight the importance of measuring markers of host and nymphal abundances over time, as we have found that seasonality is an important factor determining the dynamics between deer activity and nymphal abundance in Ireland. At a broader international level, by combining the assessment of multiple host species along with tick abundance, our data collection methodology can also inform the design of future studies predicting tick abundance and potential bite risk. Such studies could pave the way for further larger-scale, transdisciplinary investigations incorporating quantitative tick drag sampling and non-invasive host monitoring, along with contemporaneous live trapping and tick collection from a broad range of hosts, including birds and mammals. Ideally, studies of this nature would make comparisons between sites in Ireland, where there are limited mammal species available as tick hosts, with more naturally species rich woodland sites on continental Europe.\\u003c/p\\u003e\"},{\"header\":\"Declarations\",\"content\":\"\\u003cp\\u003eThe authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.\\u003c/p\\u003e \\u003cp\\u003e \\u003cstrong\\u003eEthical Approval\\u003c/strong\\u003e \\u003cp\\u003eNot applicable\\u003c/p\\u003e \\u003c/p\\u003e\\u003ch2\\u003eFunding\\u003c/h2\\u003e \\u003cp\\u003eThe PhD candidateship of which this investigation is a part was funded by The School of Natural Sciences Scholarship, University of Galway (2020\\u0026ndash;2021) and the Irish Research Council Government of Ireland Scholarship (2020\\u0026ndash;2022), and academic fees-only funding has also been provided by the Atlantic Technological University, Sligo (2023\\u0026ndash;2024).\\u003c/p\\u003e\\u003ch2\\u003eAuthor Contribution\\u003c/h2\\u003e\\u003cp\\u003eR.W. \\u0026ndash; Conceptualisation, Data collection, Data curation, Formal analysis, Funding Acquisition, Investigation, Methodology, Project Administration, WritingM.G. \\u0026ndash; Conceptualisation, Methodology, Funding Acquisition, SupervisionC.W. - Methodology, Formal Analysis, SupervisionO.H. - Data collection, Data curation, Formal analysisB.C. - Data collection, Data curation, Formal analysisC.C. - Conceptualisation, Methodology, Funding Acquisition, Supervision\\u003c/p\\u003e\\u003ch2\\u003eAcknowledgement\\u003c/h2\\u003e\\u003cp\\u003eThe authors would like to extend their gratitude to the following individuals who assisted with fieldwork data collection: Nessa Lee, Donall Folan, David Walsh, M\\u0026iacute;che\\u0026aacute;l McHugh Jewell, Liam Sheil, Elouan Treguier, Lara Marcolin, Dan Walsh, Amie Flattery, Michael Walsh, Orla Walsh, and Iseult Cummins. We offer further thanks to Donall Folan, David Walsh, M\\u0026iacute;che\\u0026aacute;l McHugh Jewell, Liam Sheil, and Elouan Treguier for their work on tick identification and processing of mammal trap footage. The authors are grateful to S\\u0026iacute;ofra Sealy for her contribution in providing ornithology expertise, and to Prof. Anetta Zintl for her expertise on ticks in Ireland. Finally, thank you to the National Parks and Wildlife Service, Coillte, the Office of Public Works, and Galway County Council for allowing access to their woodland sites.\\u003c/p\\u003e\\u003ch2\\u003eData Availability\\u003c/h2\\u003e\\u003cp\\u003eData is available upon request\\u003c/p\\u003e\"},{\"header\":\"References\",\"content\":\"\\u003col\\u003e\\n\\u003cli\\u003eVacek Z, Cukor J, Vacek S, V\\u0026aacute;clav\\u0026iacute;k T, Kybicov\\u0026aacute; K, Barto\\u0026scaron;ka J, et al. Effect of forest structures and tree species composition on common tick (\\u003cem\\u003eIxodes ricinus\\u003c/em\\u003e) abundance\\u0026mdash;Case study from Czechia. For Ecol Manag. 2023;529:120676. \\u003c/li\\u003e\\n\\u003cli\\u003eDagostin F, Tagliapietra V, Marini G, Ferrari G, Cervellini M, Wint W, et al. High habitat richness reduces the risk of tick-borne encephalitis in Europe: A multi-scale study. One Health. 2024; doi:10.1016/j.onehlt.2023.100669.\\u003c/li\\u003e\\n\\u003cli\\u003eHofmeester TR, Jansen PA, Wijnen HJ, Coipan EC, Fonville M, Prins HHT, et al. Cascading effects of predator activity on tick-borne disease risk. Proc Biol Sci. 2017; doi:10.1098/rspb.2017.0453.\\u003c/li\\u003e\\n\\u003cli\\u003eZintl A, Moutailler S, Stuart P, Paredis L, Dutraive J, Gonzalez E, et al. Ticks and Tick-borne diseases in Ireland. Ir Vet J. 2017; doi:10.1186/s13620-017-0084-y.\\u003c/li\\u003e\\n\\u003cli\\u003eFoldv\\u0026aacute;ri G. Life cycle and ecology of \\u003cem\\u003eIxodes ricinus\\u003c/em\\u003e: the roots of public health importance. In: Braks M, van Wieren S, W T, H S, editors. Ecology and prevention of \\u003cem\\u003eLyme borreliosis\\u003c/em\\u003e. Ecology and control of vector-borne diseases. 4: Netherlands: Wageningen Academic Publishers; 2016. p.31-40.\\u003c/li\\u003e\\n\\u003cli\\u003eECDC: \\u003cem\\u003eIxodes ricinus\\u003c/em\\u003e - Factsheet for experts. https://ecdc.europa.eu/en/disease-vectors/facts/tick-factsheets/ixodes-ricinus (2014). Accessed 16 Jul 2024. \\u003c/li\\u003e\\n\\u003cli\\u003eK\\u0026ouml;hler CF, Holding ML, Sprong H, Jansen PA, Esser HJ. Biodiversity in the Lyme-light: ecological restoration and tick-borne diseases in Europe. Trends Parasitol. 2023;39(5):373-85. \\u003c/li\\u003e\\n\\u003cli\\u003eJanz\\u0026eacute;n T, Hammer M, Petersson M, Dinn\\u0026eacute;tz P. Factors responsible for \\u003cem\\u003eIxodes ricinus\\u003c/em\\u003e presence and abundance across a natural-urban gradient. PLoS One. 2023; doi:10.1371/journal.pone.0285841.\\u003c/li\\u003e\\n\\u003cli\\u003eHansford KM, Wheeler BW, Tschirren B, Medlock JM. Questing \\u003cem\\u003eIxodes ricinus\\u003c/em\\u003e ticks and \\u003cem\\u003eBorrelia spp\\u003c/em\\u003e. in urban green space across Europe: A review. Zoonoses Public Health. 2022; doi:10.1111/zph.12913.\\u003c/li\\u003e\\n\\u003cli\\u003eBrugger K, Walter M, Chitimia-Dobler L, Dobler G, Rubel F. Seasonal cycles of the TBE and \\u003cem\\u003eLyme borreliosis\\u003c/em\\u003e vector \\u003cem\\u003eIxodes ricinus\\u003c/em\\u003e modelled with time-lagged and interval-averaged predictors. Exp Appl Acarol. 2017; doi:10.1007/s10493-017-0197-8.\\u003c/li\\u003e\\n\\u003cli\\u003ePf\\u0026auml;ffle M, Littwin N, Muders SV, Petney TN. The ecology of tick-borne diseases. Int J Parasitol. 2013;43(12-13):1059-77.\\u003c/li\\u003e\\n\\u003cli\\u003eDaniel M, Mal\\u0026yacute; M, Danielov\\u0026aacute; V, Kř\\u0026iacute;ž B, Nuttall P. Abiotic predictors and annual seasonal dynamics of \\u003cem\\u003eIxodes ricinus\\u003c/em\\u003e, the major disease vector of Central Europe. Parasit Vectors. 2015; doi:10.1186/s13071-015-1092-y. \\u003c/li\\u003e\\n\\u003cli\\u003eJames MC, Bowman AS, Forbes KJ, Lewis F, McLeod JE, Gilbert L. Environmental determinants of \\u003cem\\u003eIxodes ricinus\\u003c/em\\u003e ticks and the incidence of \\u003cem\\u003eBorrelia burgdorferi sensu lato\\u003c/em\\u003e, the agent of Lyme borreliosis, in Scotland. Parasitology. 2013; 140(2):237-46. \\u003c/li\\u003e\\n\\u003cli\\u003eFurness RW, Furness EN. \\u003cem\\u003eIxodes ricinus\\u003c/em\\u003e parasitism of birds increases at higher winter temperatures. J Vector Ecol. 2018; doi:10.1111/jvec.12283.\\u003c/li\\u003e\\n\\u003cli\\u003eGilbert L, Aungier J, Tomkins JL. Climate of origin affects tick (\\u003cem\\u003eIxodes ricinus\\u003c/em\\u003e) host-seeking behavior in response to temperature: implications for resilience to climate change? Ecol Evol. 2014; doi:10.1002/ece3.1014.\\u003c/li\\u003e\\n\\u003cli\\u003eGoldstein V, Boulanger N, Schwartz D, George JC, Ertlen D, Zilliox L, et al. Factors responsible for \\u003cem\\u003eIxodes ricinus\\u003c/em\\u003e nymph abundance: Are soil features indicators of tick abundance in a French region where \\u003cem\\u003eLyme borreliosis\\u003c/em\\u003e is endemic? Ticks Tick Borne Dis. 2018;9(1877-9603 (Electronic)):938-44.\\u003c/li\\u003e\\n\\u003cli\\u003eBourdin A, Dokhelar T, Bord S, van Halder I, Stemmelen A, Scherer-Lorenzen M, et al. Forests harbor more ticks than other habitats: A meta-analysis. For Ecol Manag. 2023;541:121081. \\u003c/li\\u003e\\n\\u003cli\\u003eKahl O, Gray JS. The biology of Ixodes ricinus with emphasis on its ecology Ticks Tick Borne Dis. 2023;14(2):102114.\\u003c/li\\u003e\\n\\u003cli\\u003eGray JS, Kahl O, Robertson JN, Daniel M, Estrada-Pe\\u0026ntilde;a A, Gettinby G, et al.\\u003cem\\u003e Lyme borreliosis\\u003c/em\\u003e habitat assessment. Zentralbl Bakteriol.1998;287(3):211-28.\\u003c/li\\u003e\\n\\u003cli\\u003eHumair PF, Rais O, Gern L. Transmission of \\u003cem\\u003eBorrelia afzelii\\u003c/em\\u003e from Apodemus\\u003cem\\u003e \\u003c/em\\u003emice and Clethrionomys voles to \\u003cem\\u003eIxodes ricinus\\u003c/em\\u003e ticks: differential transmission pattern and overwintering maintenance. Parasitology.1999;188(Pt1):33-42. \\u003c/li\\u003e\\n\\u003cli\\u003eHofmeester TR, Sprong H, Jansen PA, Prins HHT, van Wieren SE. Deer presence rather than abundance determines the population density of the sheep tick, \\u003cem\\u003eIxodes ricinus\\u003c/em\\u003e, in Dutch forests. Parasit Vectors. 2017; doi:10.1186/s13071-017-2370-7.\\u003c/li\\u003e\\n\\u003cli\\u003eGilbert L. How landscapes shape Lyme borreliosis risk. In: Braks M, van Wieren S, Takken W, Sprong H, editors. Ecology and Prevention of Lyme borreliosis. Ecology and Control of Vector Borne Diseases. 4: Wageningen Academic Publishers; 2016.\\u003c/li\\u003e\\n\\u003cli\\u003eGandy S, Kilbride E, Biek R, Millins C, Gilbert L. No net effect of host density on tick-borne disease hazard due to opposing roles of vector amplification and pathogen dilution. Ecol Evol. 2022; doi:10.1002/ece3.9253.\\u003c/li\\u003e\\n\\u003cli\\u003eMillins C, Gilbert L, Johnson P, James M, Kilbride E, Birtles R, et al. Heterogeneity in the abundance and distribution of \\u003cem\\u003eIxodes ricinus\\u003c/em\\u003e and \\u003cem\\u003eBorrelia burgdorferi\\u003c/em\\u003e (sensu lato) in Scotland: implications for risk prediction. Parasit Vectors. 2016; doi:10.1186/s13071-016-1875-9.\\u003c/li\\u003e\\n\\u003cli\\u003eCopernicus: CORINE land cover. https://land.copernicus.eu/pan-european/corine-land-cover. (2018, 2020). Accessed 16 Jul 2024. \\u003c/li\\u003e\\n\\u003cli\\u003eBaquero R, Telleria J. Species richness, rarity and endemicity of European mammals: a biogeographical approach. Biodiversity Conserv. 2001;10:29-44. \\u003c/li\\u003e\\n\\u003cli\\u003eKirstein F, Rijpkema S, Molkenboer M, Gray JS. Local variations in the distribution and prevalence of \\u003cem\\u003eBorrelia burgdorferi \\u003c/em\\u003esensu lato\\u003cem\\u003e \\u003c/em\\u003egenomospecies in \\u003cem\\u003eIxodes ricinus\\u003c/em\\u003e ticks. Appl Environ Microbiol. 1997; doi:10.1128/aem.63.3.1102-1106.1997.\\u003c/li\\u003e\\n\\u003cli\\u003eZintl A, Zaid T, McKiernan F, Naranjo-Lucena A, Gray J, Brosnan S, et al. Update on the presence of \\u003cem\\u003eIxodes ricinus\\u003c/em\\u003e at the western limit of its range and the prevalence of \\u003cem\\u003eBorrelia burgdorferi \\u003c/em\\u003esensu lato. Ticks Tick Borne Dis. 2020; doi:10.1016/j.ttbdis.2020.101518.\\u003c/li\\u003e\\n\\u003cli\\u003eEuropean Centre for Disease Prevention and Control; European Food Safety Authority: Field sampling methods for mosquitoes, sandflies, biting midges and ticks \\u0026ndash; VectorNet project 2014\\u0026ndash;2018. https://www.ecdc.europa.eu/sites/default/files/documents/Vector-sampling-field-protocol-2018.pdf. (2018) Accessed 16 Jul 2024.\\u003c/li\\u003e\\n\\u003cli\\u003eWearn O, Glover-Kapfer P. Camera Trapping for Conservation; A Guide to Best Practices. WWF-UK; 2017.\\u003c/li\\u003e\\n\\u003cli\\u003eCusack JJ, Dickman AJ, Rowcliffe JM, Carbone C, Macdonald DW, Coulson T. Random versus Game Trail-Based Camera Trap Placement Strategy for Monitoring Terrestrial Mammal Communities. PLoS One. 2015; doi:10.1371/journal.pone.0126373.\\u003c/li\\u003e\\n\\u003cli\\u003eKahl S, Wood C, Eibl M, Klinck H. BirdNET: A deep learning solution for avian diversity monitoring. Ecol Inform. 2021; doi:10.1016/j.ecoinf.2021.101236.\\u003c/li\\u003e\\n\\u003cli\\u003eFunosas D, Barbaro L, Schill\\u0026eacute; L, Elger A, Castagneyrol B, Cauchoix M. Assessing the potential of BirdNET to infer European bird communities from large-scale ecoacoustic data. Ecol Indic. 2024; doi:10.1016/j.ecolind.2024.112146\\u003c/li\\u003e\\n\\u003cli\\u003eMet\\u0026Eacute;ireann: Historical Data. https://www.met.ie/climate/available-data/historical-data (2024). Accessed 16 Jul 2024.\\u003c/li\\u003e\\n\\u003cli\\u003eEPA: Corine Landcover 2018-National (ITM). http://gis.epa.ie/GetData/Download (2018). Accessed 16 Jul 2024.\\u003c/li\\u003e\\n\\u003cli\\u003eGoogle Earth. Google Earth Web 2024. https://earth.google.com/web/@54.24054277,-8.38232033,114.44432255a,4352.396781d,35y,0.00000001h,13.17083798t,0r. Accessed 16 Jul 2024.\\u003c/li\\u003e\\n\\u003cli\\u003eFossitt J. A guide to habitats in Ireland. Ireland: Heritage Council; 2000.\\u003c/li\\u003e\\n\\u003cli\\u003eR Core Team. R: A language and environment for statistical computing. R Foundation for Statistical Computing. Vienna Austria; 2019.\\u003c/li\\u003e\\n\\u003cli\\u003eIBM Corp. SPSS Statistics for Windows. Version 29.0. Armonk, NY: IBM; 2023. \\u003c/li\\u003e\\n\\u003cli\\u003eBabyak MA. What you see may not be what you get: a brief, nontechnical introduction to overfitting in regression-type models. Psychosom Med. 2004;\\u003cem\\u003e \\u003c/em\\u003e66(3): 411-21. \\u003c/li\\u003e\\n\\u003cli\\u003eVoyiatzaki C, Papailia SI, Venetikou MS, Pouris J, Tsoumani ME, Papageorgiou EG. Climate Changes Exacerbate the Spread of Ixodes ricinus and the Occurrence of Lyme Borreliosis and Tick-Borne Encephalitis in Europe-How Climate Models Are Used as a Risk Assessment Approach for Tick-Borne Diseases. Int J Environ Res Public Health. 2022;19(11).\\u003c/li\\u003e\\n\\u003cli\\u003eHSE: HSE HPSC advises - Be tick aware, keep you and your family safe from Lyme disease: https://about.hse.ie/news/hse-hpsc-advises-be-tick-aware-keep-you-and-your-family-safe-from-lyme-disease/ (2024). Accesed 16 Jul 2024.\\u003c/li\\u003e\\n\\u003cli\\u003eRibeiro R, Eze J, Gunn G, Gilbert L, Macrae A, Auty H. Assessing the spatial distribution of \\u003cem\\u003eIxodes ricinus\\u003c/em\\u003e in Scotland using a Bayesian approach. Poster session presented at Vector-Borne Diseases UK (VBD2018). Norwich. \\u003c/li\\u003e\\n\\u003cli\\u003eGray JS, Kahl O, Janetzki C, Stein J. Studies on the ecology of Lyme disease in a deer forest in County Galway, Ireland. J Med Entomol. 1992;29(6):915-20.\\u003c/li\\u003e\\n\\u003cli\\u003eKilpatrick AM, Dobson ADM, Levi T, Salkeld DJ, Swei A, Ginsberg HS, et al. Lyme disease ecology in a changing world: consensus, uncertainty and critical gaps for improving control. Philos Trans R Soc B. 2017; doi:10.1098/rstb.2016.0117.\\u003c/li\\u003e\\n\\u003cli\\u003ePurser P, Wilson F, Carden R. Deer and forestry in Ireland: a review of current status and management requirements. Woodlands of Ireland.2009. https://www.woodlandsofireland.com/wp-content/uploads/DeerStrategy.pdf. Accessed 16 Jul 2024.\\u003c/li\\u003e\\n\\u003cli\\u003eCarden RF, Carlin CM, Marnell F, McElholm D, Hetherington J, Gammell MP. Distribution and range expansion of deer in Ireland. Mammal Rev. 2011;41(4):313-25.\\u003c/li\\u003e\\n\\u003cli\\u003eLiu Y, Nieuwenhui M. An analysis of habitat-use patterns of fallow and sika deer based on culling data from two estates in Co. Wicklow. Irish Forestry Journal. 2014;71\\u003c/li\\u003e\\n\\u003cli\\u003eQviller L, Risnes-Olsen N, B\\u0026aelig;rum KM, Meisingset EL, Loe LE, Ytrehus B, et al. Landscape level variation in tick abundance relative to seasonal migration in red deer. PLoS One. 2013; doi:10.1371/journal.pone.0071299.\\u003c/li\\u003e\\n\\u003cli\\u003eCat J, Beugnet F, Hoch T, Jongejan F, Prang\\u0026eacute; A, Chalvet-Monfray K. Influence of the spatial heterogeneity in tick abundance in the modeling of the seasonal activity of \\u003cem\\u003eIxodes ricinus\\u003c/em\\u003e nymphs in Western Europe. Exp Appl Acarol. 2017;71(2):115-30.\\u003c/li\\u003e\\n\\u003cli\\u003eWilhelmsson P, Pawełczyk O, Jaenson TGT, Waldenstr\\u0026ouml;m J, Olsen B, Forsberg P, et al. Three \\u003cem\\u003eBabesia\\u003c/em\\u003e species in\\u003cem\\u003e Ixodes ricinus\\u003c/em\\u003e ticks from migratory birds in Sweden. Parasit Vectors. 2021; doi:10.1186/s13071-021-04684-8.\\u003c/li\\u003e\\n\\u003cli\\u003eHeyman E, Gunnarsson B. Management effect on bird and arthropod interaction in suburban woodlands. BMC Ecol. 2011; doi:10.1186/1472-6785-11-8.\\u003c/li\\u003e\\n\\u003c/ol\\u003e\"}],\"fulltextSource\":\"\",\"fullText\":\"\",\"funders\":[],\"hasAdminPriorityOnWorkflow\":false,\"hasManuscriptDocX\":true,\"hasOptedInToPreprint\":true,\"hasPassedJournalQc\":\"\",\"hasAnyPriority\":false,\"hideJournal\":true,\"highlight\":\"\",\"institution\":\"\",\"isAcceptedByJournal\":false,\"isAuthorSuppliedPdf\":false,\"isDeskRejected\":\"\",\"isHiddenFromSearch\":false,\"isInQc\":false,\"isInWorkflow\":false,\"isPdf\":false,\"isPdfUpToDate\":true,\"isWithdrawnOrRetracted\":false,\"journal\":{\"display\":true,\"email\":\"info@researchsquare.com\",\"identity\":\"researchsquare\",\"isNatureJournal\":false,\"hasQc\":true,\"allowDirectSubmit\":true,\"externalIdentity\":\"\",\"sideBox\":\"\",\"snPcode\":\"\",\"submissionUrl\":\"/submission\",\"title\":\"Research Square\",\"twitterHandle\":\"researchsquare\",\"acdcEnabled\":true,\"dfaEnabled\":false,\"editorialSystem\":\"\",\"reportingPortfolio\":\"\",\"inReviewEnabled\":false,\"inReviewRevisionsEnabled\":true},\"keywords\":\"Ixodes ricinus, abundance, tick bite risk, abiotic factors, host factors, avian hosts\",\"lastPublishedDoi\":\"10.21203/rs.3.rs-4848879/v1\",\"lastPublishedDoiUrl\":\"https://doi.org/10.21203/rs.3.rs-4848879/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\\u003e\\u003cem\\u003eIxodes ricinus\\u003c/em\\u003e (Linnaeus 1758) vectors several important diseases in Europe, and the nymphal abundance in an area is an important factor determining tick bite risk. While interactions between abiotic, habitat, and vertebrate host factors and this tick species are generally well understood in continental Europe, this is not the case in Ireland, a highly fragmented and vertebrate depauperate region of Europe. This study examines the abiotic, habitat and host factors predicting nymphal abundance in such a setting. Our findings may provide insights for possible future changes in \\u003cem\\u003eI. ricinus\\u003c/em\\u003e vector ecology on continental Europe given current predictions of future vertebrate diversity loss.\\u003c/p\\u003e\\n\\u003cp\\u003e\\u003cstrong\\u003eMethods:\\u003c/strong\\u003e\\u003c/p\\u003e\\n\\u003cp\\u003e15 woodland sites in Ireland were surveyed over three years (2020-2022) wherein abiotic and habitat factors were determined and tick abundance recorded. Concurrently, mammal and birdsong activity data were collected for each site across multiple visits. Generalised linear mixed models were used to identify the most important factors predicting\\u003cem\\u003e I. ricinus \\u003c/em\\u003eabundance.\\u003c/p\\u003e\\n\\u003cp\\u003e\\u003cstrong\\u003eResults:\\u003c/strong\\u003e\\u003c/p\\u003e\\n\\u003cp\\u003eNymphal \\u003cem\\u003eI. ricinus\\u003c/em\\u003e abundance was driven by seasonality, with peak abundance occurring in April. Abiotic and habitat factors featured less than expected in models predicting nymphal abundance, but mean minimum winter temperature was found to have an inverse predictive relationship with adult tick abundance. While \\u003cem\\u003eI. ricinus\\u003c/em\\u003e nymphs were significantly more abundant at sites where deer were present, at visit level, there was an inverse predictive relationship between deer activity events the week of a site visit and nymphal abundance. Modelling individual host species as predictors of nymphal abundance also identified increased mean robin birdsong events for the previous year to be a predictor of decreased nymphal abundance.\\u003c/p\\u003e\\n\\u003cp\\u003e\\u003cstrong\\u003eConclusions:\\u003c/strong\\u003e\\u003c/p\\u003e\\n\\u003cp\\u003eSeasonality predicted nymphal tick abundance more robustly than any other abiotic variable. Seasonality was also the driving factor behind the relationships seen between deer activity and nymphal abundance. This highlights the importance of understanding the seasonal changes in dynamics between \\u003cem\\u003eI. ricinus\\u003c/em\\u003e abundance and host activity, a less well-studied area. Furthermore, the identification of European robin as a predictor of nymphal abundance in woodland sites confirms the important relationship between passerine bird species and \\u003cem\\u003eI. ricinus\\u003c/em\\u003e in Ireland.\\u003c/p\\u003e\",\"manuscriptTitle\":\"Ixodes ricinus in Ireland: exploring the links between environmental factors, host species activity and tick abundance in an area of Europe with limited potential vertebrate hosts\",\"msid\":\"\",\"msnumber\":\"\",\"nonDraftVersions\":[{\"code\":1,\"date\":\"2024-09-16 00:37:21\",\"doi\":\"10.21203/rs.3.rs-4848879/v1\",\"editorialEvents\":[{\"type\":\"communityComments\",\"content\":0}],\"status\":\"published\",\"journal\":{\"display\":true,\"email\":\"info@researchsquare.com\",\"identity\":\"researchsquare\",\"isNatureJournal\":false,\"hasQc\":true,\"allowDirectSubmit\":true,\"externalIdentity\":\"\",\"sideBox\":\"\",\"snPcode\":\"\",\"submissionUrl\":\"/submission\",\"title\":\"Research Square\",\"twitterHandle\":\"researchsquare\",\"acdcEnabled\":true,\"dfaEnabled\":false,\"editorialSystem\":\"\",\"reportingPortfolio\":\"\",\"inReviewEnabled\":false,\"inReviewRevisionsEnabled\":true}}],\"origin\":\"\",\"ownerIdentity\":\"f71b53e9-297e-4cd1-a49a-f7754aed89f5\",\"owner\":[],\"postedDate\":\"September 16th, 2024\",\"published\":true,\"recentEditorialEvents\":[],\"rejectedJournal\":[],\"revision\":\"\",\"amendment\":\"\",\"status\":\"posted\",\"subjectAreas\":[],\"tags\":[],\"updatedAt\":\"2024-09-16T00:37:23+00:00\",\"versionOfRecord\":[],\"versionCreatedAt\":\"2024-09-16 00:37:21\",\"video\":\"\",\"vorDoi\":\"\",\"vorDoiUrl\":\"\",\"workflowStages\":[]},\"version\":\"v1\",\"identity\":\"rs-4848879\",\"journalConfig\":\"researchsquare\"},\"__N_SSP\":true},\"page\":\"/article/[identity]/[[...version]]\",\"query\":{\"redirect\":\"/article/rs-4848879\",\"identity\":\"rs-4848879\",\"version\":[\"v1\"]},\"buildId\":\"qtupq5eGEP_6zYnWcrvyt\",\"isFallback\":false,\"isExperimentalCompile\":false,\"dynamicIds\":[84888],\"gssp\":true,\"scriptLoader\":[]}","source_license":"CC-BY-4.0","license_restricted":false}