Camera trapping for small mammals: the case of a non- native shrew

Camera trapping for small mammals: the case of a non- native shrew | 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 Camera trapping for small mammals: the case of a non- native shrew C. R. Sharpe, R.A. Hill, S. Y. Diggins, P. A. Stephens This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7083063/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 30 Jan, 2026 Read the published version in European Journal of Wildlife Research → Version 1 posted 10 You are reading this latest preprint version Abstract In recent decades, motion sensor camera traps have revolutionised wildlife monitoring as a cost-effective strategy requiring less time investment than traditional monitoring methods. While medium-to-large body sized mammals are captured at sufficient resolution to permit confident species identifications, small mammal species (mice, voles, and shrews) are difficult to distinguish in conventional camera trap imagery. Since camera traps represent a potential solution for overcoming spatial and temporal constraints of traditional small mammal survey methodologies (live trapping), novel designs have materialised in recent years to adjust camera traps for observing smaller animals. In this research, we further refined an existing design, the Littlewood box, and investigated the optimal bait strategy to maximise small mammal detections in the Northeast of England within the currently known range of the non-native greater white toothed shrew, Crocidura russula . We found no significant difference in the probability of detection of small mammal species by bait type, but there were greater numbers of captures of shrew species at traps baited with mealworms. We conclude that the use of bait is associated with a greater number of captures for all small mammal species observed compared to non-baited traps. Despite the cameras being deployed in the centre of the known range of C. russula in Britain, this species was present at a lower proportion of sites than native small mammals. Camera trap small mammal monitoring shrew Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 Figure 8 Figure 9 Introduction In Britain, as in the rest of the world, declines in species abundance have proceeded at an alarming rate over the past 50 years. Driven largely by agricultural land use changes and climate change, hundreds of species have experienced abundance declines in the UK, and almost 1500 native species are threatened with extinction, resulting in the UK being considered a nature-depleted nation (Burns et al., 2023 ). Halting and reversing such declines in native species has become a legally binding target of the UK government, following the implementation of the Environment Act, which will require targeted management interventions (DEFRA, 2021 ). As well as taxonomically widespread abundance declines in the UK, there have been accompanying distribution changes for native species. One group where such a change has been notably drastic is small mammals (here defined as mouse, vole, and shrew species). Among mammals that have experienced average abundance declines of 7% since 1970 (Burns et al., 2023 ), small mammals have experienced the largest percentage decrease in distribution compared to other assemblages (deer, bats, or mid-sized mammals), owing to declines in five of seven species analysed: bank vole, common shrew, field vole, harvest mouse, and water shrew (Coomber et al., 2021 ). Due to their position as economically damaging pest species, small mammals are often the subjects of conflicting conservation and eradication agendas (Ferreira and Delibes-Mateos, 2012 ). However, it is widely recognised that small mammals play vital roles in ecosystem functioning. Changes to small mammal communities can alter the structuring of vegetation (Hurst et al., 2014 ; Poe et al., 2019 ); small rodents, in particular, play an essential role in seed dispersal and plant establishment processes (Godó et al., 2022 ). As a key prey item, population cycles in small mammals can also drive numerical and functional responses in predators (Avotins et al., 2023 ), and can equally be driven by the density of predators (Korpimäki et al., 2005 ). Contractions in the distributions of the UK’s small mammals are troubling, therefore, not only because of the loss of small mammals, but also because of the wider environmental implications of such losses. Assessing the state of nature is made possible by volunteer-based species monitoring schemes in the UK for birds, mammals, butterflies, moths, marine fish, and freshwater invertebrates (Burns et al., 2023 ). Likely due to difficulties in distinguishing between small mammal species, they are typically the least recorded of mammals despite their large population sizes (Croft et al., 2017 ). For small mammals, monitoring has historically relied on live trapping, a field methodology conducted by trained researchers and less accessible to the volunteer involvement that has granted large scale monitoring of other taxa. Physically capturing animals with this technique allows for unambiguous species identification as well as the retrieval of data concerning sex, age, and reproductive stage. Live trapping with capture-recapture methods enables inferences about the abundance of small mammals (Castañeda et al., 2018 ), and, when traps are visited at frequent intervals throughout the day, daily activity patterns can be obtained (Kalinin and Aleksandrov, 2023 ). However, live trapping is labour-intensive, as traps must be visited frequently to avoid mortality of captured animals. This limits the geographic and temporal scope of surveys and is demanding of human resources. Unfortunately mortality is a frequent result of live-trapping surveys, particularly for species with high metabolisms, such as shrews (Shonfield et al., 2013 ; Stromgren and Sullivan, 2014 ). Alternatively, small mammal presence can be non-invasively inferred using environmental DNA sampling (Verhees et al., 2024 ) or track tubes where footprints are left by passing animals (Duffie et al., 2019 ), but both techniques require a relatively high degree of researcher expertise; track tubes, in particular, involve a high degree of uncertainty in species assignments. For landscape-scale assessments, owl pellet dissection can provide estimates of small mammal community composition, but this technique permits only broad scale inferences as variations between and within local sites are disregarded and, due to owl habitat and dietary preferences, certain sites and species can be underrepresented (Heisler et al., 2016 ). For larger body sized mammals, recent decades have seen a revolution in monitoring at scale due to the development of increasingly user-friendly and affordable camera trapping methods. By collecting observations of passing animals at any time of the day, motion-sensor cameras overcome challenges of observing nocturnal and elusive species, traits shared by both large and small mammals. Unlike other survey techniques, where animal encounters are often fleeting, images serve as records of the encounter and can be reviewed after the time of capture, facilitating the implementation of larger scale monitoring initiatives for larger body sized mammals (e.g. MammalWeb (Hsing et al., 2022 ), Snapshot Serengeti (Swanson et al., 2015 ), or eMammal (McShea et al., 2016 )). Due to the cost-effectiveness of CTs relative to live trapping (De Bondi et al., 2010 ), and their effectiveness at scaling up monitoring for larger bodied mammals, a recent burst of innovations has extended this method to small mammal surveys. Further, relative to live trapping, deducing activity patterns of captured animals is much simpler from CT records that are timestamped at the time of observation, facilitating investigations into interspecific activity patterns of small mammals (Probst and Probst, 2023 ). To best observe small mammals with CTs, it is necessary to reduce the field of view by confining CTs to tunnels, buckets, or boxes, and adding a close focus lens in front of the existing lens to reduce the focal distance. With these adaptations, CTs have been used to record high resolution images of mice, voles, shrews, small mustelids, and marsupials (Gracanin et al., 2019 ; Littlewood et al., 2021 ; McCleery et al., 2014 ; Mos and Hofmeester, 2020 ; Smaal and van Manen, 2022 ). Despite the success of these studies, adapting CTs for small mammals can be regarded as an approach in its infancy, yet to receive wide uptake. Furthermore, although previous efforts rely on bait or lures to increase detection probability, no rigorous consideration has been given to the potential for bait choice to influence the inferences made about the composition of small mammal communities. We address this knowledge gap in the present study by varying bait treatments between CTs and examining the effect on number of captures as well as detection probability of small mammal species. Adapted camera traps have been deployed in northern Scotland to successfully assess small mammal occurrence in different habitats (Littlewood et al., 2021 ). As this survey considers the same target species as Littlewood et al. ( 2021 ), their design was closely followed in the construction of adapted CTs for this project. The north of England has previously been characterised as a ‘data desert’ for British mammals owing to a relatively lower survey effort than other parts of the country (Croft et al., 2017 ). By conducting our CT survey in this region, we aim to improve regional mammal records with high-resolution observations of small mammal species. Moreover, our application of adapted CTs to the north of England is incidentally relevant because of the detection of the non-native greater white-toothed shrew, Crocidura russula (hereafter, GWTS). While DNA analysis confirmed the presence of the GWTS near Sunderland, in 2021, dated photographs indicate the species has been present in the area since 2015 (Bond et al., 2022 ). The GWTS has demonstrated itself to be a formidable invasive species in Ireland over the past 20 years, with a radial expansion rate of approximately 5.5km a year since being first detected in 2008 (McDevitt et al., 2014 ; Tosh et al., 2008 ). Based on current reports from owl pellet dissection and live trapping in Britain, it appears that the GWTS is largely confined to the area between Durham and Sunderland in the North East of England (Smith et al., in review), in addition to a population in Nottinghamshire (McDevitt, 2023 ). The species occupies a wide range of habitats including farmland, hedgerows, forest, woodland, urban areas, and gardens. The competitive abilities of the GWTS are evident within its native habitat where it has been expanding its range at the expense of congenerics (Vogel et al., 2002 ) as well as out-with its native range in Ireland, where it is implicated in the disappearance of native pygmy shrew Sorex minutus from invaded zones as well as observed negative effects on soil surface invertebrate communities (McDevitt et al., 2014 ; Montgomery et al., 2023 ). Based on their well-documented invasion in Ireland, a rapid risk assessment recommended a dedicated monitoring effort to elucidate potential impacts on native small mammals in Britain, particularly the seemingly vulnerable pygmy shrew (McDevitt, 2023 ). We therefore chose to conduct our trial of adapted small mammals CTs in areas where the non-native species has been detected, contributing to knowledge on the status of native species in ‘invaded’ zones. To promote further application of adapted CTs, we assess differences in the rate of capture of small mammals by various bait treatments to offer insight into deployment strategies for adapted CTs. A combination of peanut butter and oats is regarded as a universal bait in small mammal surveys, and has generally has high success at attracting small mammals indiscriminately (Kok et al., 2013 ; Paull et al., 2011 ). However, it seems unlikely that one bait has universal appeal to small mammals with different dietary preferences. Live trapping studies have demonstrated that including mealworms in bait treatments reduces the accidental mortality of captured shrew species (Do et al., 2013 ; Stromgren and Sullivan, 2014 ), but little consideration has been given to the potential bias involved in selecting one bait type over another. We predict that including mealworms in bait treatments will improve the detection of shrew species, since this more closely resembles their typical insect food source than grain-based baits, and that all baits will lead to higher capture rates for all species than un-baited controls. Methods Adapted camera traps and technical settings We constructed Littlewood boxes based on Littlewood et al ( 2021 ) and used Browning Recon Force Elite HP5 Camera Traps (Browning Trail Cameras, USA). Two prototype tunnels were compared in a pilot study to support the final design of tunnels used in this analysis. The pilot tested a box open at one end against a tunnel with openings at both ends. Animals made use of both entrances where the tunnel was open at both ends and, due to the added advantage of allowing small mammals to escape potential predators if cornered in boxes, we opted for a tunnel design with two open ends, departing slightly from the design of Littlewood et al ( 2021 ). Each tunnel was constructed using three 60cm x 20cm x2.5cm wooden sides and a 60cm x 20cm Perspex roof. The Perspex roof, introduced by Littlewood et al. ( 2021 ), ensures more light is available to the CT within the tunnel, permitting colour images to be taken during the day and facilitating improved species identification. Placing CTs inside one end of the tunnel removes the requirement for weatherproofing necessary when cameras are outside of the tunnel as in Littlewood et al. ( 2021 ). The tunnels were slightly longer than that employed by Littlewood et al ( 2021 ) to compensate for the cameras being inside the box, thus maintaining the camera’s field of view relative to the Littlewood box. At one end of each tunnel, we drilled holes into each side approximately 7cm from the tunnel floor to accommodate a python lock that threaded through the CT’s back brace, allowing the CT to be suspended in the air. At the same end of the tunnel, we added braces that permitted us to tilt the camera toward a bait station (Fig. 1 ). The python lock passed through the camera and tunnel, and could be secured to a tree, log, or fencepost at the survey site. The specifications of our tunnels catered to the specific CTs used; surveys using alternative camera models should adjust measurements accordingly. Camera traps were programmed to record bursts of 3 photos in a sequence per trigger of the motion-sensor, with a 1-minute delay between triggers to balance the desire to obtain sufficient footage while avoiding excessive activity that would fill up SD cards and deplete battery life. The IR flash power was set to ‘economy’. Although Littlewood et al. ( 2021 ) found it necessary to cover the infra-red flash of the cameras used in their survey, in a pilot study we found our cameras did not require this adaptation, owing to the “illuma-smart” technology that overcomes problems of overexposure of close-up animals. In front of the existing camera lens, we affixed a close-focus + 4 macro camera filter (37mm diameter) with blu-tac (Fig. 1 ). Eight rechargeable batteries powered each camera trap and were replenished as required. Camera deployment Survey sites were selected within the GWTS’s known range in the northeast of England, based on presence/absence surveys of owl pellets by local researchers (Ian Bond and Terry Coult) (Fig. 2 ). Currently, evidence suggests that GWTS range (excluding the Nottinghamshire population) is constrained between Sunderland and Durham. During the fieldwork period, a specimen from Peterlee was identified after capture by a domestic cat belonging to a Durham University colleague, resulting in the decision to survey a further site (Castle Eden Dene), which was not within the boundaries suggested by the owl pellet survey. Chosen sites depended on survey permissions but, in total, eight sites were surveyed between May and October 2024: Durham Botanic Gardens, Rainton Meadows, Herrington Country Park, Tunstall Hills, Hetton Lyons Country Park, Castle Eden Dene, Sherburn Village and Sherburn Grange Farm (Fig. 2 ). Two habitats were surveyed with 12 cameras each at Rainton Meadows: grassland and woodland. Apart from this case at Rainton Meadows there were only 12 unique camera locations at each site. Within each site, we selected locations away from areas of major public access and with appropriate structures (e.g., fence post, tree or log) to which to lock CT tunnels. Within each cluster, each CT was spaced approximately 3–6 metres apart from its nearest neighbour depending on structures available to attach the lock. This dense placement was intended to ensure that all traps within a cluster were within an area that reflects a small mammal home range. Three clusters were deployed simultaneously at each survey site so that, during a deployment, twelve adapted CTs were active in three distinct clusters. We aimed to maximise heterogeneity of cluster placements within sites so, at the largest site, Castle Eden Dene, the clusters were up to 1.25 kilometres apart; at the smallest site, Sherburn Farm, clusters were as little as 50 metres apart. Bait treatment Aluminium weighing boats (56mm diameter) were used as bait trays in the CT tunnels, fixed in place with drawing pins. Four bait treatments were tested: (1) peanut butter and oats, (2) dried mealworms, (3) a mixture of 1 and 2, and (4) a control treatment with an empty bait tray. Camera traps were deployed in groups of 4, with each tunnel corresponding to one of the four bait treatments. Each group of 4 is referred to as a ‘cluster’. Tunnels received the same bait treatment throughout the survey to control for any effect of scent that may linger in boxes and act as a lure. Bait was replenished every 3–6 days, except for one period (21/05/2024–03/06/2024) where no bait was re-filled due to researcher unavailability. Overall, cameras were deployed at 108 unique locations during the survey, corresponding to 27 clusters (3 clusters at each of 7 sites, and two groups of 3 clusters at a further site). Among the surveyed sites, farmland, woodland and grassland habitats were represented. Based on records from the National Biodiversity Network (NBN) Atlas, the small mammals (mice, voles, and shrews) recorded in this region since 2015 includes the shrew species pygmy shrew S. minutus , common shrew S. araneus , water shrew N. fodiens , and GWTS, and the rodents: wood mouse Apodemus sylvaticus , yellow-necked mouse A. flavicollis , house mouse Mus musculus , harvest mouse Micromys minutus , fat dormouse Glis glis , hazel dormouse Muscardinus avellanarius , bank vole Myodes glareolus , field vole Microtus agrestis , water vole Arvicola amphibus , and Orkney vole Microtus arvalis (NBN Trust, 2025 ). As a publicly available data repository that collates records from many different sources nationally, the NBN is vulnerable to erroneous records of rare or unlikely species, so we note that there are some species unlikely to be present in this area (yellow-necked mouse, harvest mouse, fat dormouse, hazel dormouse, and Orkney vole). Image collection/processing At each re-baiting visit, images were copied in the field from SD cards onto a hard drive and subsequently uploaded to MammalWeb (Hsing et al., 2022 ) at the first available opportunity. MammalWeb offers a user-friendly portal for image classification, where bursts of images taken at each camera trigger are automatically presented to the classifier as a ‘sequence’ of images taken within 10 seconds of each other, for classification, accompanied by a list of plausible species based on the host project. Sequences from this fieldwork were uploaded to the ‘Shrew monitoring in Britain’ subproject within a larger ‘Small Mammal Camera Trapping’ project. Therefore, the accompanying list of species in the classification interface included small mammal species observable in the UK. In addition, MammalWeb passes uploaded images through Megadetector (Beery et al., 2019 ) to screen out images that contain humans or are devoid of wildlife, minimising the time required to process images that do not contain target species. In a preliminary analysis, the accuracy of Megadetector was assessed over 6,771 image sequences that had received human classification by C.S. (lead author). Of sequences that Megadetector classifies as containing ‘Nothing’ (936 sequences), there were only 18 in which C.S. alternatively identified an animal. Therefore, for the dataset as a whole, we accepted with a high degree of confidence that there are unlikely to be animals missed in the images classed as ‘Nothing’ by Megadetector (~ 3500 sequences). Although the project was open to any volunteer contributor to MammalWeb, only classifications by C.S. are used in the current analysis. Data analysis If a camera had malfunctioned during a given deployment, data from other cameras within that cluster and deployment were excluded to equalise effort across bait treatments. Data from cameras in Sherburn Village were included, only up to the last data collection timepoint prior to the theft of 8 of the 12 cameras. During data processing, each capture was assigned a ‘time since bait’ that described the numeric time (hours) elapsed between the bait treatment being refilled and the timestamp on the CT sequence, allowing us to investigate the effect of re-baiting frequency on small mammal detections. Single season occupancy models were used to examine the variables influencing occupancy and detection of small mammals at camera traps. This approach assumes that the presence or absence of a species at a site (occupancy) is constant throughout the survey period, that there are no false positive detections, and that detections are independent between sites (MacKenzie et al., 2002 ). Models were specified using the ‘occu’ function in the ‘unmarked’ package in R (Fiske and Chandler, 2011 ; Kellner et al., 2023 ). The models were based on detection histories that, for each species, consisted of a matrix where rows correspond to CT sites and columns correspond to repeated survey periods at those sites which, in this case, were specified as 24-hour survey periods. Occupancy models have two sub models: the first models the occupancy probability of a species as a function of site-level covariates, and the second models detection probability of that species as a function of site and survey level covariates. The global model allowed detection to vary according to bait type and time elapsed since bait, and occupancy to vary according to habitat type. This global model was fitted for wood mouse, bank vole, common shrew, pygmy shrew, and field vole, but for water shrew and the greater white-toothed shrew, a paucity of observations meant that only the null model (providing estimates for occupancy and detection probabilities without covariate influence) could be fitted with confidence. For species for which the global model could be fitted, all possible submodels were assessed using the ‘dredge’ function in the ‘MuMIn’ package within R (Bartoń, 2024 ). To inform the predictions reported here, all models within 6 ΔAIC c of the best model were considered, excluding models with additional predictors relative to the best model (Richards et al., 2011 ). In the case that there was more than one best model according to these specifications, the reported predictions here came from averaging the model predictions according to their AICc-based weight using the modavgPred function in the AICcmodavg package (Mazerolle, 2023 ). For each species’ best model, model fit was evaluated with a parametric bootstrap goodness-of-fit test (MacKenzie and Bailey, 2004 ) using 1,000 iterations. The parametric bootstrap enhances the utility of the occupancy models by assessing whether they provide a realistic representation of the data (p > 0.05), or if they display a significant lack of fit to the data (p < 0.05). To assess broad differences between bait types in the rate of capture, the number of captures at each camera in the first 72 hours of deployment were aggregated. This period was selected since 72 hours was the minimum deployment duration and so was comparable between all camera deployments. Data were not normally distributed, so non-parametric Kruskal-Wallis tests were used to assess differences in the number of captures per deployment between bait treatments, using a significance level of p < 0.05. This was first done for all species and subsequently for each species individually. Post-hoc Pairwise Wilcoxon rank-sum tests with Bonferroni correction were conducted to investigate significant relationships between the bait treatments regarding the mean number of captures. To provide inference regarding the impact of non-native GWTS, observational records from the National Biodiversity Network (NBN) Atlas for County Durham (NBN Trust, 2025 ) were downloaded for each of the small mammal species observed on CT images. This download is included as supplementary material (Supplementary 1), as well as details of the data providers (Supplementary 2). Records included in the NBN database come from a diverse range of sources, such as live trapping studies as well as roadkill observations or ad hoc sightings, and therefore the records were considered a suitable source against which we compared the relative frequencies of observation of the species captured in our CTs. The total number of records for each species between 2015–2024 in County Durham was plotted on a log scale against the total number of captures from the CT survey, also on a log scale, to compare relative frequencies of observation. Results Across all CTs, 50,328 sequences were captured and uploaded to MammalWeb. After passing through Megadetector, 46,824 sequences were deemed likely (Pr > 0.1) to contain an animal and were classified by C.S. A total of 27 species were identified, and for sequences where an animal could not be identified to the species level, the label assigned was one of the following: vole (unknown species), unidentified bird, small rodent (unknown species), shrew sp, other, nothing, human, or don’t know. After excluding sequences from partial deployments and where the individual could not be identified to species-level, 30,368 small mammal-containing sequences were included in this analysis, with 27 sequences that included more than one species (Table 1). These data, excluding clusters where there were malfunctions, corresponded to 2,161 trap days (24-hour periods). Seven small mammal (mice, vole, or shrew) species were identified, with body size, ear size, colouration, and tail length and width being features that facilitated classification from the high-resolution imagery obtained (Figure 3). Wood mouse, bank vole, common shrew, and pygmy shrew were detected at all survey sites (Table 2). Table 1. Total number of CT sequences in which small mammal species were observed, as well as the number used in occupancy analyses after excluding sequences from partial deployments. Species Total number of sequences Analysis sequences Wood mouse 16123 15474 Bank vole 11838 11601 Pygmy shrew 1863 1700 Common shrew 1404 1366 Field vole 229 223 GWTS 48 27 Water shrew 5 4 Small rodent (unknown species) 1147 - Vole (unknown species) 322 - Shrew (unknown species) 279 - Occupancy modelling For wood mouse, bank vole, and pygmy shrew, the model selection process resulted in a single best model being used to provide the inferences presented for each, but for field vole and common shrew there were two candidate models over which model averaging was used to generate predictions (Supplementary material 3). Detection probabilities for all species were significantly affected by time since bait (Table 3). There was a significant negative correlation between the time elapsed since bait and the detection probability, although the magnitude of this differed by species (Figures 4 and 5). Parametric bootstrap goodness of fit tests for each model indicated no significant lack of fit (p > 0.05, Table 3), suggesting that the best model(s) fit the data well for each species. In the first 24 hours following bait treatment, the detection probability was highest for wood mouse (Figure 4), and lowest for field vole (Table 4 and Figure 5). Habitat type had no significant effect on the occupancy of wood mouse, bank vole, or pygmy shrew, but common shrew and field vole were less likely to occupy woodland than farmland and grassland (Figure 6 and Table 4). For wood mouse, there was a statistically significant interaction between bait type and time since bait, with a higher detection probability maintained over time for the peanut butter and oats bait compared to all other treatments (Figure 4). The null model for water shrew predicted an occupancy of 0.009 (0.001-0.06) and detection probability of 0.59 (0.19-0.90), with the model providing a good fit to the data (chi-square statistic = 537, bootstrapped p-value = 0.59). For the GWTS , occupancy was predicted to be 0.09 (0.04-0.19) with a corresponding detection probability of and 0.24 (0.11-0.45), again with a good fit to the data (chi-square statistic = 517, bootstrapped p-value = 0.50). GWTS were recorded at cameras with MW, MW + PB, and PB treatments, while the water shrew was recorded at only one camera with a MW treatment. Table 3. Description of predictors included in the best model for each species. Measures of model fit included are AICc-based model weight, the chi-square statistic and bootstrap p-value from parametric bootstrap goodness of fit tests (p < 0.05 indicates significant lack of model fit). The effect size of the continuous variable ‘days since bait’ is included, as this was a significant detection predictor for every species. Occupancy Detection AICc-based model weight Chi-square test statistic Bootstrap p-value Species Habitat Bait type Days since bait Bait type*days since bait Wood mouse No Yes -1.12 Yes 0.81 122 0.73 Bank vole No No -0.60 No 0.59 191 0.52 Pygmy shrew No No -0.28 No 0.69 451 0.56 Common shrew Yes No -0.58 No 0.85 387 0.56 No No -0.58 No 0.07 386 0.53 Field vole Yes No -0.40 No 0.74 528 0.59 No No -0.40 No 0.21 506 0.55 Table 4. Estimates of occupancy and detection probability from the best model(s) for small mammal species, with 95% confidence intervals in brackets. Species Occupancy probability Detection probability in first 24 hours since bait Woodland Grassland Farmland Wood mouse 0.98 (0.93-0.995) - - Figure 4 Bank vole 0.83 (0.75-0.89) - - 0.95 (0.91-0.97) Pygmy shrew 0.55 (0.46-0.65) - - 0.62 (0.53-0.71) Common shrew 0.39 (0.29-0.51) 0.84 (0.52-0.96) 0.63 (0.42-0.80) 0.83 (0.75-0.89) Field vole 0.27 (0.16-0.42) 0.59 (0.21-0.88) 0.41 (0.22-0.64) 0.51 (0.36-0.65) Number of captures Across all species and sites surveyed, the number of captures differed significantly between bait treatments (H (3) = 174.33, p < 0.05). The number of captures for cameras with no bait was significantly lower than for cameras with PB (p < 0.05), MW (p < 0.05) or PB+MW (p < 0.05); however, across bait types, there were no significant differences (p = 1) (Figure 7). The significantly lower capture rate in un-baited traps was also recorded for individual species: wood mouse (H (3) = 125.52, p < 0.05), bank vole (H (3) = 78.19, p < 0.05), common shrew (H (3) = 34.19, p < 0.05) and pygmy shrew (H (3) = 26.29, p < 0.05) (Figure 8). Captures of water shrew were isolated to one CT which had a MW treatment, so additional statistical analyses were not informative. For field vole, there was no significant difference between any bait treatment (H (3) = 4.16, p = 0.25), as was the case for the GWTS (H (3) = 2.67, p = 0.44). For all shrew species observed, the mean number of captures was greatest at the MW bait treatment (Figure 8). Relative capture rates of GWTS and native small mammals Frequency of observation of native small mammals using adapted CTs correlated positively with frequency of reports of those species in County Durham via NBN (Figure 9). However, the GWTS was documented in our CT survey more frequently than would be predicted based on the number of reports in the NBN database. Further, we did not identify the following species in any CT sequences: water vole, house mouse, harvest mouse, Orkney vole, hazel dormouse, yellow-necked mouse or fat dormouse although they have been reported at various frequencies in the region via the NBN atlas. Discussion We trialled a relatively new method of surveying small mammals, partly motivated by the urgent need for appraisal of the UK’s native small mammals in the presence of the non-native GWTS, but more directly motivated by a requirement to innovate new methods for scaling up data collection on small mammals that have not been historically well recorded in the UK. For the species observed - wood mouse, bank vole, field vole, common shrew, pygmy shrew, water shrew and GWTS - we were able to deduce the proportion of sites occupied, and to investigate the effect of different bait treatments on detection probabilities and number of captures. Although using bait significantly increases the number of captures, bait type had no significant effect on the probability of detection for any species. Apart from the water shrew, a specialist of wetland habitats that were not represented in this survey, the GWTS had the lowest predicted occupancy of all small mammal species observed. We discuss our findings in relation to the success of the adapted CT methodology, implications of our bait treatment experiment for informing the optimal use of adapted CTs and, finally, what the results of our survey suggest about the potential GWTS invasion in the UK. Adapting camera traps for small mammals The adapted CT design trialled in this survey permitted observation of small mammals and reliably captured species that we expected to be present. The quality of images obtained during our survey rival the current best methods (Fig. 3 ) and provide a method superior to techniques where cameras are placed on poles and pointed at bare patches of ground. Although pole-mounted cameras can facilitate capture and identification of rodents and marsupials ranging from dunnarts to kangaroos (De Bondi et al., 2010 ; Rendall et al., 2014 ), it would be difficult to distinguish confidently between smaller mammal species, such as shrews, photographed this way. Additionally, the pole-mounting method is not as feasible in areas of denser ground vegetation (Littlewood et al., 2021 ). Recently, pole mounted cameras that are lower to the ground (50cm), have a close focus lens and were tilted toward bait stations have proved successful for monitoring small mammal communities in Germany (Rimbach et al., 2025 ). Downward facing cameras in buckets have better facilitated distinction between voles, mice, and rats (Dueser et al., 2025 ; McCleery et al., 2014 ), and downward facing cameras have been used within tunnels to distinguish between voles, shrews, and stoats beneath snow (Soininen et al., 2015 ). This design has permitted year-round continuous monitoring of small mammals in Norway, providing insight on their seasonal and diel activity patterns (Lindsø et al., 2025 ). The face-on design used here and in Littlewood et al. ( 2021 ) better allows researchers to distinguish species within groups of small mammals (i.e., different species of mice, voles or shrews), a process that necessitates higher resolution of smaller physical features. Recent research has suggested that, with camera trap boxes, species identification is only possible for larger bodied small mammals (> 25cm in length) (Fink and Jachowski, 2025 ); however, we concur with Littlewood et al. ( 2021 ) that boxes provide a good method for small species discrimination within the UK small mammal assemblage. We added a bait station that encouraged animals to spend more time in the camera’s high focus area, as suggested by Littlewood et al. ( 2021 ). Since animals were frequently photographed in this zone, this likely aided the capacity for species distinctions to be made. Similarly high resolution images have been obtained for arboreal sugar gliders ( Petaurus breviceps ) and brown antechinus ( Antechinus stuartii ) using tunnels placed in trees (Gracanin et al., 2022 ), and PVC tunnels on the ground have permitted observation of multiple shrew, vole, and mouse species in the Netherlands (Smaal and van Manen, 2022 ). Our survey thus adds to a growing body of literature that demonstrates that only a few minor adjustments to conventional CTs can enable more efficient small mammal surveys with confident species identifications. The correlation between the frequency with which native species were observed during this survey and the frequency with which they have been reported in the last 10 years in County Durham via the NBN Atlas speaks to the potential of CTs to capture a sample of the small mammal population representative of that which would be observed by other means. This is encouraging for future uptake of the method for small mammal surveys, since there are several advantages relative to the convention of live trapping. Firstly, reduced field work time results in a more cost-effective strategy than live-trapping (De Bondi et al., 2010 ; Villette et al., 2016 ; Verhees et al., 2024 ), providing a financial incentive for researchers. Secondly, as an ‘open’ trap, CTs also better enable surveys to record multiple species in-between researcher visits, making them more efficient at species detection. More abundant species have a higher probability of encountering traps first and therefore could block live traps from recording rarer species, especially when cameras are programmed to have a delay between triggers. When rare species are of concern, as they often are in ecological assessments, CTs are therefore more likely to detect them. Further, there are ethical incentives for prioritising non-invasive survey methods to avoid disturbing animals. This is particularly relevant for shrews that have heightened vulnerability to mortality in live trapping surveys (Shonfield et al., 2013 ). Given the climate of concern regarding native UK shrew species in the presence of the non-native GWTS, the avoidance of trap mortality represents a major advantage of CTs. Several species reported in County Durham in the past decade were not recorded during this CT survey. Most of these species (yellow-necked mouse, harvest mouse, fat dormouse, hazel dormouse, Orkney vole) are unlikely or rarely encountered in County Durham and are most likely to be misidentifications. This raises an important feature of CT monitoring: images serve as vouchers of the encounter, so records can be better verified than by alternative survey methodologies. The water vole and house mouse are likely present in the region, but their non-detection is explained because they exist in more specialised habitats that were not represented in this survey. Optimising detection by adapted CTs Seven species of small mammal were detected during this survey (Table 1 ) and as expected, CT stations with bait recorded significantly more captures per deployment of every species than CTs with no bait, irrespective of bait type. The mean number of captures for all shrew species (common shrew, pygmy shrew, water shrew, GWTS) was highest at CTs with the mealworm-only bait treatment. Given that including mealworms does not result in significantly lower captures of rodents’ wood mouse, bank vole, or field vole (Fig. 6), mealworms seem to be a worthwhile inclusion in bait treatments. Time since baiting negatively affected the detection of all species so, for longer-term studies, it may be preferable to use an alternative baiting method that requires less frequent researcher visits. One solution could be to use a scent lure instead of bait, to extend attraction to traps temporally. Salmon oil performed this function in a survey investigating the optimal bait strategy for weasel detection with camera traps (Bergeson et al., 2025 ), and automated scent dispersers at CTs have proven successful for minimising required researcher visits at sites with challenging and dangerous conditions when surveying rare carnivores (Long et al., 2024 ). Both represent solutions for maintaining high detection probabilities while reducing the requirement for frequent researcher visits. While baited traps recorded significantly more captures than unbaited traps, bait type had no significant effect on the probability of detection for any species. This contrasted with our expectations. If indeed non-baited traps could provide the same power of detection, this would represent cost-savings to researchers in terms of bait costs and researcher visits to traps, as well as a lower number of image sequences to process. However, this result could also be an artefact of the proximity of CTs within a cluster. Due to the pronounced effect of time since bait on the detection probability of all species considered, it is fair to make the conclusion that including bait improves detection probability. Although increasing the distance between CTs may reduce any effect of interacting scent lures, it is uncertain what distance could achieve this without extending beyond an area that represents a contiguous small mammal home range. For instance, GWTS has an estimated home range with a radius of 5–7 metres (Talleraas, 2021 ), and so we would be cautious about increasing the distance between traps. In general, the uncertainty in small mammal home ranges complicates study design. Even if we could optimise based on the home range sizes of the shrew species observed, the scale of home ranges is larger for mice or voles (Frafjord, 2024 ; Hummell et al., 2022 ). Even within species, there could be dynamism in the home range size depending on whether surveys are carried out in the breeding season or not, and whether the individual is male or female (Frafjord, 2024 ). Advances in small mammal tracking with GPS or radio collars continue to provide clearer inference regarding small mammal home ranges (Frafjord, 2024 ; Hummell et al., 2022 ; Makuya and Schradin, 2023 ), but the remaining uncertainty is problematic, and trap proximity may have confounded the effect of different bait treatments. Status of the GWTS in the UK The occupancy of GWTS was predicted to be 0.06 (0.03–0.15) representing the lowest value for all observed species, excluding the water shrew. In contrast, pygmy shrews were recorded in over 1,000 sequences, with an occupancy of 0.55 (0.46–0.65); we found a similar result for common shrews, with an occupancy of 0.39 (0.29–0.51), 0.84 (0.52–0.96) and 0.63 (0.42–0.80) in woodland, grassland, and farmland habitats, respectively. For the native small mammals observed in this survey, relative frequencies from our CTs correlated well with NBN Atlas records for the region. This result, combined with a predicted occupancy an order of magnitude lower than native shrew species, should provide some reassurance that there is, as yet, no detectable negative impact of the non-native species, despite our survey being carried out in the centre of its currently known range. We recorded more sequences of GWTS than would be predicted in the region based on the rate of reporting via the NBN Atlas (Fig. 8), probably because our survey was carried out in the centre of its known distribution, and because the species is likely under-reported by the public. Although the potential of GWTS to rapidly displace pygmy shrews has been demonstrated in Ireland, this is not yet evident in our study area. In Ireland, the disappearance of pygmy shrews from sites where GWTS has become established can happen in as little as 1 year after contact (McDevitt et al., 2014 ) whereas, in Sunderland where GWTS has likely been present for several years (Bond et al., 2022 ), pygmy shrews remain present. Concern regarding the presence of GWTS in Britain is justified given the species’ potential to negatively impact native biodiversity, but the ecological contexts of Ireland and the UK are substantially different. As the sole species of shrew present in Ireland prior to the appearance of GWTS, Irish pygmy shrews’ naivety to resource competition might account for the species’ rapid displacement (Browett et al., 2023 ). In the initial stages of the invasion in Ireland, the current explanation is that GWTS exploited larger invertebrate prey items that were not utilised by pygmy shrews. After depleting these resources, a switch to smaller prey items on which pygmy shrews depend created resource competition, from which the GWTS emerged as the stronger competitor (Browett et al., 2023 ). In Britain, this process could be buffered by the prior existence of a multi-shrew community where niche separation has facilitated coexistence through resource partitioning. Rather than arriving to abundant large invertebrate food resources, GWTS would immediately have been in competition with the similarly sized common shrew, potentially reducing its rapid expansion in Britain (McDevitt, 2023 ). In its native range, GWTS coexists with pygmy shrews, with both species common on agricultural land on the island of Belle Ile, amongst a small mammal assemblage comparable to Ireland’s (McDevitt et al., 2014 ). Consequently, it is hard to infer the specific causes of pygmy shrew exclusion in Ireland. Considering an ecological context more like that of the UK, further insight can be drawn from Norway’s experience of this species. GWTS was recorded for the first time in Norway in 2012, the northernmost record for this species in the world (Kooij and Nyfors, 2023 ). Here, the species was first discovered via a public record. Subsequently, targeted media calls elicited more observations from the public, with some specimens being forwarded to researchers for further analysis. In reaction to the increasing records, a dedicated survey was carried out using camera traps in addition to pitfall traps and records from the public. GWTS was reported to occupy a proportion of the survey area comparable to that of several native small mammals (field vole, common shrew, pygmy shrew), but no negative impact was discernible (Talleraas, 2021 ). Further, GWTS and pygmy shrew were present at the same sites. While our results suggest that GWTS occupies a lower proportion of the survey area than native UK small mammal species, our finding of no detectable impact on native small mammals in the UK aligns with this result from Norway. Together, these results strengthen the hypothesis that different ecological contexts can explain differential impacts of non-native GWTS between Ireland, the UK, and Norway. Aside from impacts on small mammals, GWTS can also act as a reservoir host for pathogens that cause leptospirosis, a zoonotic disease. A novel Leptospira pathogen was identified in the invasive Irish GWTS population, which might implicate disease-mediated interactions in the disappearance of the Irish pygmy shrew, although exact consequences are yet to be determined (Nally et al., 2016 ). The presence of GWTS has also been associated with negative impacts on invertebrates: reduced species richness, reduced abundance, shorter arthropod body length and lower arthropod biomass have been reported in invaded relative to uninvaded zones in Ireland, sparking concern regarding disruption to farmland ecosystem services such as nutrient cycling, as well as potential impacts on farmland birds (Montgomery et al., 2023 ). From a social and economic viewpoint, GWTS’s tendency to take up residence in homes could also present pest control challenges (McDevitt, 2023 ). Conclusion Future research should prioritise further monitoring to better understand the spread of GWTS in the UK and continue to monitor for potential negative interactions with native species. The design for adapted CTs pioneered by Littlewood et al ( 2021 ) and refined in this research offers a template for adapting commercially available CTs for the purpose of small mammal monitoring, and we recommend that if bait is applied, mealworms should be included to maximise the capture of shrews. Expanding the survey area beyond what was possible within this project could provide further information on any further range expansion of GWTS. Taking an example from Norway, targeted media calls for citizens to submit any shrew observations to a citizen science platform effectively surveyed a much larger area than possible by researchers alone (Kooij and Nyfors, 2023 ) and a similar scheme in the UK could provide evidence of populations not currently known to researchers. Planned genetic analysis of the UK GWTS population will be insightful in determining the likely source of the non-native population (McDevitt, 2023 ) and, hopefully, can inform management solutions to prevent such transport in the future. Our survey results, and those of Talleraas ( 2021 ) in Norway, provide some reassurance that GWTS’s impacts might not always be as severe as reported in Ireland. Nevertheless, we would recommend caution in extrapolating the results of this small-scale survey, given the known capabilities of GWTS as a formidable competitor in Ireland. There remain several ways in which this species could cause damage, beyond its potential impacts on small mammal communities. Statements and Declarations Acknowledgements We gratefully acknowledge the contributions of Vivien Kent, Terry Coult, and Ian Bond, who kindly shared small mammal live-trapping survey results with us that guided the deployments of camera traps in this work. Further thanks go to Tom Stephenson and David McGregor at Sunderland City Council, as well as Jim Cokill at the Durham Wildlife Trust, and Joe Davies at Castle Eden Dene National Nature Reserve for facilitating access to survey sites during the summer of 2024. Our thanks also go to Dr Christine Howard providing a record of the non-native shrew Crocidura russula out-with its then known range, aiding our survey site selection. Funding: Financial support for the purchase of field equipment was received from the Animal and Plant Health agency (APHA). Competing interests: The authors have no competing interests to declare that are relevant to the content of this article. References Avotins A, Ķerus V, Aunins A (2023) Numerical Response of Owls to the Dampening of Small Mammal Population Cycles in Latvia. Life 13:572. https://doi.org/10.3390/life13020572 Bartoń K (2024) MuMIn: Multi-model inference (manual) Beery S, Morris D, Yang S (2019) Efficient Pipeline for Camera. https://doi.org/10.48550/arXiv.1907.06772 . Trap Image Review Bergeson SM, Kays R, Jachowski DS, Anderson CD, Williamson CR, Burket A, Butfiloski JW, Cheeseman AE, Cotey SR, Dennison CC, Erb JD, Farris ZJ, Fies ML, Joyce MJ, Olfenbuttel C, Sasse B, Smith LM, Tabora JA, Zimova M (2025) Efficacy of baits and lures for weasel detection. Wildl Soc Bull 49:e1580. https://doi.org/10.1002/wsb.1580 Bond I, Gilford E, McDevitt A, Young M, Coomber F (2022) First records of the greater white-toothed shrew Crocidura russula from Great Britain. Mammal Com 8. https://doi.org/10.59922/AACI6532 Browett SS, Synnott R, O’Meara DB, Antwis RE, Browett SS, Bown KJ, Wangensteen OS, Dawson DA, Searle JB, Yearsley JM, McDevitt AD (2023) Resource competition drives an invasion-replacement event among shrew species on an island. J Anim Ecol 92:698–709. https://doi.org/10.1111/1365-2656.13855 Burns et al (2023) State of Nature 2023 - report on the UK’s current biodiversity [WWW Document]. State of Nature. URL https://stateofnature.org.uk/ (accessed 02/08/24) Castañeda I, Pisanu B, Díaz M, Rézouki C, Baudry E, Chapuis J-L, Bonnaud E (2018) Minimising trapping effort without affecting population density estimations for small mammals. Mamm Biol 93:144–152. https://doi.org/10.1016/j.mambio.2018.10.004 Coomber FG, Smith BR, August TA, Harrower CA, Powney GD, Mathews F (2021) Using biological records to infer long-term occupancy trends of mammals in the UK. Biol Conserv 264:109362. https://doi.org/10.1016/j.biocon.2021.109362 Croft S, Chauvenet ALM, Smith GC (2017) A systematic approach to estimate the distribution and total abundance of British mammals. PLoS ONE 12:e0176339. https://doi.org/10.1371/journal.pone.0176339 De Bondi N, White J, Stevens M, Cooke R (2010) A comparison of the effectiveness of camera trapping and live trapping for sampling terrestrial small-mammal communities. Wildl Res 37:456–465. https://doi.org/10.1071/WR10046 DEFRA (2021) World-leading Environment Act becomes law [WWW Document]. GOV.UK. URL https://www.gov.uk/government/news/world-leading-environment-act-becomes-law (accessed 02/11/25) Do R, Shonfield J, McAdam AG (2013) Reducing accidental shrew mortality associated with small-mammal livetrapping II: a field experiment with bait supplementation. J Mammal 94:754–760. https://doi.org/10.1644/12-MAMM-A-242.1 Dueser RD, Porter JH, Moncrief ND (2025) The continuing search for a better mouse trap: Two tests of a practical, low-cost camera trap for detecting and observing small mammals. PLoS ONE 20:e0309252. https://doi.org/10.1371/journal.pone.0309252 Duffie DR, Gitzen RA, Sharp NW, Turner AJ (2019) Effectiveness and Accuracy of Track Tubes for Detecting Small-Mammal Species Occupancy in Southeastern Herbaceous Wetlands and Meadows. Southeast Nat 18:130–146. https://doi.org/10.1656/058.018.0109 Ferreira C, Delibes-Mateos M (2012) Conflictive management of small mammals considered as pests: A long way to evidence-based policy making. Curr Zool 58:353–357. https://doi.org/10.1093/czoolo/58.2.353 Fink M, Jachowski D (2025) Comparison of three camera trap designs for sampling small mammals. Mamm Res 1–8. https://doi.org/10.1007/s13364-025-00780-7 Fiske I, Chandler R (2011) unmarked: An R Package for Fitting Hierarchical Models of Wildlife Occurrence and Abundance. J Stat Softw 43:1–23. https://doi.org/10.18637/jss.v043.i10 Frafjord K (2024) Dynamic home range of the grey-sided vole Craseomys rufocanus : a pilot study. BMC Zool 9:16. https://doi.org/10.1186/s40850-024-00209-9 Godó L, Valkó O, Borza S, Deák B (2022) A global review on the role of small rodents and lagomorphs (clade Glires) in seed dispersal and plant establishment. Glob Ecol Conserv 33:e01982. https://doi.org/10.1016/j.gecco.2021.e01982 Gracanin A, Gracanin V, Mikac KM (2019) The selfie trap: A novel camera trap design for accurate small mammal identification. Ecol Manag Restor 20:156–158. https://doi.org/10.1111/emr.12345 Gracanin A, Minchinton TE, Mikac KM (2022) Estimating the density of small mammals using the selfie trap is an effective camera trapping method. Mammal Res 67:467–482. https://doi.org/10.1007/s13364-022-00643-5 Heisler LM, Somers CM, Poulin RG (2016) Owl pellets: a more effective alternative to conventional trapping for broad-scale studies of small mammal communities. Methods Ecol Evol 7:96–103. https://doi.org/10.1111/2041-210X.12454 Hsing PY, Hill RA, Smith GC, Bradley S, Green SE, Kent VT, Mason SS, Rees J, Whittingham MJ, Cokill J, Stephens PA, MammalWeb Citizen S (2022) Large-scale mammal monitoring: The potential of a citizen science camera-trapping project in the United Kingdom. Ecol Solut Evid 3. https://doi.org/10.1002/2688-8319.12180 Hummell GF, Li AY, Mullinax JM (2022) Very small collars: an evaluation of telemetry location estimators for small mammals. Anim Biotelemetry 10:1–14. https://doi.org/10.1186/s40317-022-00301-2 Hurst ZM, McCleery RA, Collier BA, Silvy NJ, Taylor PJ, Monadjem A (2014) Linking changes in small mammal communities to ecosystem functions in an agricultural landscape. Mamm Biol 79:17–23. https://doi.org/10.1016/j.mambio.2013.08.008 Kalinin AA, Aleksandrov DY (2023) Residents and Nonresidents: Daily Activity of Small Mammals in Contrasting Biotopes. Russ J Ecol 54:392–401. https://doi.org/10.1134/S1067413623050053 Kellner KF, Smith AD, Royle JA, Kéry M, Belant JL, Chandler RB (2023) The unmarked R package: Twelve years of advances in occurrence and abundance modelling in ecology. Methods Ecol Evol 14:1408–1415. https://doi.org/10.1111/2041-210X.14123 Kok A, Parker D, Barker N (2013) Rules of attraction: the role of bait in small mammal sampling at high altitude in South Africa. Afr Zool 48:84–95. https://doi.org/10.1080/15627020.2013.11407571 van der Kooij J, Nyfors E (2023) Citizen science reveals the first occurrence of the greater white-toothed shrew Crocidura russula in Fennoscandia. Mammalia 87:442–450. https://doi.org/10.1515/mammalia-2023-0042 Korpimäki E, Norrdahl K, Huitu O, Klemola T (2005) Predator-induced synchrony in population oscillations of coexisting small mammal species. Proc. R. Soc. B-Biol. Sci. 272, 193–202. https://doi.org/10.1098/rspb.2004.2860 Lindsø LK, Rivrud IM, König EA, Herland A, Mysterud A (2025) Diel Niche of Sympatric Small Mammals Revealed by Year-Round Camera Trapping. Ecol Evol 15:e71590. https://doi.org/10.1002/ece3.71590 Littlewood NA, Hancock MH, Newey S, Shackelford G, Toney R (2021) Use of a novel camera trapping approach to measure small mammal responses to peatland restoration. Eur J Wildl Res 67. https://doi.org/10.1007/s10344-020-01449-z Long RA, MacKay P, Sauder JD, Sinclair M, Aubry KB, Raley CM (2024) An overwinter protocol for detecting wolverines and other carnivores at camera traps paired with automated scent dispensers. Ecol Evol 14:e11290. https://doi.org/10.1002/ece3.11290 MacKenzie DI, Bailey LL (2004) Assessing the fit of site-occupancy models. J Agri Biol Env Stat 9:300–318. https://doi.org/10.1198/108571104X3361 MacKenzie DI, Nichols JD, Lachman GB, Droege S, Andrew Royle J, Langtimm CA (2002) Estimating Site Occupancy Rates When Detection Probabilities Are Less Than One. Ecol 83:2248–2255. https://doi.org/10.1890/0012-9658 (2002)083[2248:ESORWD]2.0.CO;2 Makuya L, Schradin C (2023) Measuring range sizes in a 100-g rodent: mini-GPS are more reliable than transmitters, but the location error reduces reliability. Mamm Biol 103:455–465. https://doi.org/10.1007/s42991-023-00365-4 Mazerolle MJ (2023) AICcmodavg: Model selection and multimodel inference based on (Q)AIC(c) (manual) McCleery RA, Zweig CL, Desa MA, Hunt R, Kitchens WM, Percival HF (2014) A Novel Method for Camera-Trapping Small Mammals. Wildl Soc Bull 38:887–891. https://doi.org/10.1002/wsb.447 McDevitt A (2023) Rapid Risk Assessment Summary Sheet: Crocidura russula (greater white-toothed shrew) [WWW Document]. URL https://www.nonnativespecies.org/assets/Uploads/Crocidura-russula-RRA-final.pdf (accessed 11/21/24) McDevitt AD, Montgomery WI, Tosh DG, Lusby J, Reid N, White TA, McDevitt CD, O’Halloran J, Searle JB, Yearsley JM (2014) Invading and Expanding: Range Dynamics and Ecological Consequences of the Greater White-Toothed Shrew ( Crocidura russula ) Invasion in Ireland. PLoS ONE 9:e100403. https://doi.org/10.1371/journal.pone.0100403 McShea WJ, Forrester T, Costello R, He Z, Kays R (2016) Volunteer-run cameras as distributed sensors for macrosystem mammal research. Landsc Ecol 31:55–66. https://doi.org/10.1007/s10980-015-0262-9 Montgomery WI, Montgomery SSJ, O’Neill J, Reid N (2023) Invasion of farmland hedgerows by non-native small mammals is associated with lower soil surface invertebrate diversity, abundance, body size and biomass. Biol Invasions. https://doi.org/10.1007/s10530-023-03199-w Mos J, Hofmeester TR (2020) The Mostela : an adjusted camera trapping device as a promising non-invasive tool to study and monitor small mustelids. Mamm Res 65:843–853. https://doi.org/10.1007/s13364-020-00513-y Nally JE, Arent Z, Bayles DO, Hornsby RL, Gilmore C, Regan S, McDevitt AD, Yearsley J, Fanning S, McMahon BJ (2016) Emerging Infectious Disease Implications of Invasive Mammalian Species: The Greater White-Toothed Shrew ( Crocidura russula ) Is Associated With a Novel Serovar of Pathogenic Leptospira in Ireland. Plos Negl Trop Dis 10:e0005174. https://doi.org/10.1371/journal.pntd.0005174 NBN Trust (2025) The National Biodiversity Network (NBN) Atlas. https://ror.org/00mcxye41 Paull DJ, Claridge AW, Barry SC (2011) There’s no accounting for taste: bait attractants and infrared digital cameras for detecting small to medium ground-dwelling mammals. Wildl Res 38:188–195. https://doi.org/10.1071/WR10203 Poe N, Stuble KL, Souza L (2019) Small mammal herbivores mediate the effects of soil nitrogen and invertebrate herbivores on grassland diversity. Ecol Evol 9:3577–3587. https://doi.org/10.1002/ece3.4991 Probst R, Probst R (2023) High Frequency of Apodemus Mice Boosts Inverse Activity Pattern of Bank Voles, Clethrionomys glareolus , through Non-Aggressive Intraguild Competition. Anim 13:981. https://doi.org/10.3390/ani13060981 Rendall AR, Sutherland DR, Cooke R, White J (2014) Camera Trapping: A Contemporary Approach to Monitoring Invasive Rodents in High Conservation Priority Ecosystems. PLoS ONE 9:e86592. https://doi.org/10.1371/journal.pone.0086592 Richards SA, Whittingham MJ, Stephens PA (2011) Model selection and model averaging in behavioural ecology: the utility of the IT-AIC framework. Behav Ecol Sociobiol 65:77–89. https://doi.org/10.1007/s00265-010-1035-8 Rimbach R, Heinze K, Poorthuis L, Petit J, Dammhahn M (2025) City life does not change a small mammal community composition. Wildl Res 52. https://doi.org/10.1071/WR24196 Shonfield J, Do R, Brooks RJ, McAdam AG (2013) Reducing accidental shrew mortality associated with small-mammal livetrapping I: an inter- and intrastudy analysis. J Mammal 94:745–753. https://doi.org/10.1644/12-MAMM-A-271.1 Smaal M, van Manen W (2022) Detecting and monitoring small mammals with trail cameras. Lutra 65:247–257 Smith G, Bond IF, Coult T, Henderson D, Graham C, Brand E, McDevitt AD, Stephens PA, Webb S, Larsen-Daw M (in review) ‘Investigating the greater white-toothed shrew invasion in Great Britain’ https://doi.org/10.21203/rs.3.rs-5849552/v1 Soininen EM, Jensvoll I, Killengreen ST, Ims RA (2015) Under the snow: a new camera trap opens the white box of subnivean ecology. Remote Sens Ecol Conserv 1:29–38. https://doi.org/10.1002/rse2.2 Stromgren EJ, Sullivan TP (2014) Influence of pitfall versus Longworth livetraps, bait addition, and drift fences on trap success and mortality of shrews. Acta Theriol 59:203–210. https://doi.org/10.1007/s13364-013-0149-6 Swanson A, Kosmala M, Lintott C, Simpson R, Smith A, Packer C (2015) Snapshot Serengeti, high-frequency annotated camera trap images of 40 mammalian species in an African savanna. Sci Data 2:150026. https://doi.org/10.1038/sdata.2015.26 Talleraas SM (2021) Estimating and analysing the occupancy of the introduced shrew Crocidura russula in Norway (Master thesis). Norwegian University of Life Sciences, Ås Tosh DG, Lusby J, Montgomery WI, O’halloran J (2008) First record of greater white-toothed shrew Crocidura russula in Ireland. Mamm Rev 38:321–326. https://doi.org/10.1111/j.1365-2907.2008.00130.x Verhees JJF, van der Putten TAW, van Hoof PH, Heijkers D, Lemmers P, Esser HJ, de Boer WF (2024) Comparing the effectiveness of short-focal camera trapping, live trapping, and soil eDNA for surveying small mammals: A case study on Eurasian water shrew ( Neomys fodiens ). Eur J Wildl Res 70:13. https://doi.org/10.1007/s10344-023-01760-5 Villette P, Krebs CJ, Jung TS, Boonstra R (2016) Can camera trapping provide accurate estimates of small mammal ( Myodes rutilus and Peromyscus maniculatus ) density in the boreal forest? J Mammal 97:32–40. https://doi.org/10.1093/jmammal/gyv150 Vogel P, Jutzeler S, Rulence B, Reutter BA (2002) Range expansion of the greater white-toothed shrew Crocidura russula in Switzerland results in local extinction of the bicoloured white-toothed shrew C. leucodon . Acta Theriol 47:15–24. https://doi.org/10.1007/BF03193562 Additional Declarations No competing interests reported. Supplementary Files Supplementary1.csv Supplementary2.xlsx Supplementarymaterial3.docx Cite Share Download PDF Status: Published Journal Publication published 30 Jan, 2026 Read the published version in European Journal of Wildlife Research → Version 1 posted Editorial decision: Revision requested 31 Dec, 2025 Reviews received at journal 02 Nov, 2025 Reviewers agreed at journal 21 Oct, 2025 Reviewers agreed at journal 20 Oct, 2025 Reviewers agreed at journal 18 Oct, 2025 Reviewers agreed at journal 17 Oct, 2025 Reviewers invited by journal 15 Oct, 2025 Editor assigned by journal 15 Jul, 2025 Submission checks completed at journal 15 Jul, 2025 First submitted to journal 09 Jul, 2025 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-7083063","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":532949354,"identity":"efacf506-d12c-485a-8de2-622bc49d2484","order_by":0,"name":"C. R. Sharpe","email":"data:image/png;base64,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","orcid":"","institution":"Durham University","correspondingAuthor":true,"prefix":"","firstName":"C.","middleName":"R.","lastName":"Sharpe","suffix":""},{"id":532949356,"identity":"50dcc1c6-a1c3-4d9b-a6f6-eacef53a0748","order_by":1,"name":"R.A. Hill","email":"","orcid":"","institution":"Durham University","correspondingAuthor":false,"prefix":"","firstName":"R.A.","middleName":"","lastName":"Hill","suffix":""},{"id":532949357,"identity":"3e01c0e4-758c-4894-b509-0b19b6f72c4f","order_by":2,"name":"S. Y. Diggins","email":"","orcid":"","institution":"Durham University","correspondingAuthor":false,"prefix":"","firstName":"S.","middleName":"Y.","lastName":"Diggins","suffix":""},{"id":532949361,"identity":"e5bc6a1c-005f-49ab-b6a4-8aa64f8d25f2","order_by":3,"name":"P. A. Stephens","email":"","orcid":"","institution":"Durham University","correspondingAuthor":false,"prefix":"","firstName":"P.","middleName":"A.","lastName":"Stephens","suffix":""}],"badges":[],"createdAt":"2025-07-09 10:53:37","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-7083063/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-7083063/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1007/s10344-026-02052-4","type":"published","date":"2026-01-30T15:59:08+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":94728698,"identity":"bc6dd53d-660f-4d42-97e1-8da21efc04b8","added_by":"auto","created_at":"2025-10-30 07:04:10","extension":"docx","order_by":0,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":4846247,"visible":true,"origin":"","legend":"","description":"","filename":"CorrectedSubmissionJuly25.docx","url":"https://assets-eu.researchsquare.com/files/rs-7083063/v1/1918ffac27c827dc0667f098.docx"},{"id":94687351,"identity":"a1882ab8-0037-4720-abc3-b01777b9738e","added_by":"auto","created_at":"2025-10-29 15:47:46","extension":"json","order_by":1,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":6789,"visible":true,"origin":"","legend":"","description":"","filename":"33b2ee26461642978f67c83cb6d34ecf.json","url":"https://assets-eu.researchsquare.com/files/rs-7083063/v1/434bd4e1862845703ab08fda.json"},{"id":94729109,"identity":"86bf5ca4-9d07-48ca-a15e-313d326375b5","added_by":"auto","created_at":"2025-10-30 07:04:37","extension":"csv","order_by":2,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":2325763,"visible":true,"origin":"","legend":"","description":"","filename":"Supplementary1.csv","url":"https://assets-eu.researchsquare.com/files/rs-7083063/v1/0600ae68306c31d9d3188e6b.csv"},{"id":94729257,"identity":"329f07d1-9360-405f-a8f3-150b27824bbd","added_by":"auto","created_at":"2025-10-30 07:04:43","extension":"xlsx","order_by":3,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":14755,"visible":true,"origin":"","legend":"","description":"","filename":"Supplementary2.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-7083063/v1/de24caa63c6f1283c6e423a2.xlsx"},{"id":94687360,"identity":"9adedb8e-1eac-4acc-8751-c316daf5e6f7","added_by":"auto","created_at":"2025-10-29 15:47:46","extension":"docx","order_by":4,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":36290,"visible":true,"origin":"","legend":"","description":"","filename":"Supplementarymaterial3.docx","url":"https://assets-eu.researchsquare.com/files/rs-7083063/v1/f7f8b040bd3d2aa8f660c897.docx"},{"id":94728305,"identity":"948968a8-9e66-48f4-9b70-a33b17770d5f","added_by":"auto","created_at":"2025-10-30 07:03:29","extension":"xml","order_by":5,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":182012,"visible":true,"origin":"","legend":"","description":"","filename":"33b2ee26461642978f67c83cb6d34ecf1enriched.xml","url":"https://assets-eu.researchsquare.com/files/rs-7083063/v1/19e42fd4a690e693b856d143.xml"},{"id":94728300,"identity":"6a854955-21a3-48ee-b7f8-7d9848dc8cf6","added_by":"auto","created_at":"2025-10-30 07:03:29","extension":"png","order_by":6,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":604920,"visible":true,"origin":"","legend":"","description":"","filename":"floatimage1.png","url":"https://assets-eu.researchsquare.com/files/rs-7083063/v1/83f6f841218f3f7a424cb60d.png"},{"id":94729260,"identity":"7846ac31-d0f7-424a-8633-bd69170f6f0a","added_by":"auto","created_at":"2025-10-30 07:04:43","extension":"jpeg","order_by":7,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":38378,"visible":true,"origin":"","legend":"","description":"","filename":"floatimage10.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-7083063/v1/a733107cd3a1e1af9f997f6f.jpeg"},{"id":94728581,"identity":"d4d024df-ae1a-4955-a8f8-03d3d83b8779","added_by":"auto","created_at":"2025-10-30 07:04:02","extension":"jpeg","order_by":8,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":177044,"visible":true,"origin":"","legend":"","description":"","filename":"floatimage11.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-7083063/v1/9163098a3d03164f60d9d306.jpeg"},{"id":94728323,"identity":"223e022f-a870-469c-9b75-9878c6a5c937","added_by":"auto","created_at":"2025-10-30 07:03:33","extension":"jpeg","order_by":9,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":282984,"visible":true,"origin":"","legend":"","description":"","filename":"floatimage12.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-7083063/v1/61eca346343d379c74a2bd51.jpeg"},{"id":94728644,"identity":"7aadd064-cab6-4271-b4a6-ed96c734daf5","added_by":"auto","created_at":"2025-10-30 07:04:08","extension":"jpeg","order_by":10,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":138271,"visible":true,"origin":"","legend":"","description":"","filename":"floatimage13.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-7083063/v1/132e56641b1762921285cb7d.jpeg"},{"id":94728450,"identity":"0582329b-d84c-4f35-b68a-d1aaed16f442","added_by":"auto","created_at":"2025-10-30 07:03:50","extension":"jpeg","order_by":11,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":427432,"visible":true,"origin":"","legend":"","description":"","filename":"floatimage2.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-7083063/v1/8546660c9fe1e1b833390c5e.jpeg"},{"id":94728654,"identity":"4415fcd7-7cb2-4991-81ad-bfd97eabe1f9","added_by":"auto","created_at":"2025-10-30 07:04:08","extension":"png","order_by":12,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":1982689,"visible":true,"origin":"","legend":"","description":"","filename":"floatimage3.png","url":"https://assets-eu.researchsquare.com/files/rs-7083063/v1/82db3adb845a64fed5741e9b.png"},{"id":94728341,"identity":"36cdf7e4-9bab-4a2e-baeb-19788f2ce23d","added_by":"auto","created_at":"2025-10-30 07:03:35","extension":"jpeg","order_by":13,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":178242,"visible":true,"origin":"","legend":"","description":"","filename":"floatimage4.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-7083063/v1/f582e4acabe3239ff1df63c0.jpeg"},{"id":94687382,"identity":"a7cabf53-1db5-4816-9f6a-245a6b91a9ac","added_by":"auto","created_at":"2025-10-29 15:47:46","extension":"jpeg","order_by":14,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":56600,"visible":true,"origin":"","legend":"","description":"","filename":"floatimage5.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-7083063/v1/cee4cb308bb74e4dc91245e3.jpeg"},{"id":94728065,"identity":"42bdbc10-6d67-4eca-ae84-7ce034055005","added_by":"auto","created_at":"2025-10-30 07:02:59","extension":"jpeg","order_by":15,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":55719,"visible":true,"origin":"","legend":"","description":"","filename":"floatimage6.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-7083063/v1/8d48c21f240e5b2ae427522b.jpeg"},{"id":94687381,"identity":"3958c5fb-bdfa-4e5e-8ee3-52b8174aa4f2","added_by":"auto","created_at":"2025-10-29 15:47:46","extension":"jpeg","order_by":16,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":56318,"visible":true,"origin":"","legend":"","description":"","filename":"floatimage7.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-7083063/v1/a79532f777634fe1e245b013.jpeg"},{"id":94728124,"identity":"fba606e9-0ac9-4a5e-94bf-ec805447f008","added_by":"auto","created_at":"2025-10-30 07:03:07","extension":"jpeg","order_by":17,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":50305,"visible":true,"origin":"","legend":"","description":"","filename":"floatimage8.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-7083063/v1/119fb144a0b2fff7ea1c264e.jpeg"},{"id":94687369,"identity":"da47b20a-a6c9-48b6-aae7-8de7475090a9","added_by":"auto","created_at":"2025-10-29 15:47:46","extension":"jpeg","order_by":18,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":40620,"visible":true,"origin":"","legend":"","description":"","filename":"floatimage9.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-7083063/v1/c564798c82191284380bb95c.jpeg"},{"id":94728538,"identity":"4f9ea056-a44c-4517-996d-6f881bc2e744","added_by":"auto","created_at":"2025-10-30 07:04:00","extension":"png","order_by":19,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":114874,"visible":true,"origin":"","legend":"","description":"","filename":"Onlinefloatimage1.png","url":"https://assets-eu.researchsquare.com/files/rs-7083063/v1/c6839645a694adaf12d5cd8b.png"},{"id":94729129,"identity":"25b13607-56aa-4dfb-836c-40b36e793b07","added_by":"auto","created_at":"2025-10-30 07:04:38","extension":"png","order_by":20,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":22213,"visible":true,"origin":"","legend":"","description":"","filename":"Onlinefloatimage10.png","url":"https://assets-eu.researchsquare.com/files/rs-7083063/v1/bec867fce5d32edb06736ffc.png"},{"id":94687377,"identity":"15e88ae2-0e5c-4b8b-bad8-95030d1af384","added_by":"auto","created_at":"2025-10-29 15:47:46","extension":"png","order_by":21,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":26910,"visible":true,"origin":"","legend":"","description":"","filename":"Onlinefloatimage11.png","url":"https://assets-eu.researchsquare.com/files/rs-7083063/v1/759fca61d0ea3767d5941b0b.png"},{"id":94687387,"identity":"a1ad9533-af19-4c2a-b9d8-93fa3dfe874d","added_by":"auto","created_at":"2025-10-29 15:47:46","extension":"png","order_by":22,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":53040,"visible":true,"origin":"","legend":"","description":"","filename":"Onlinefloatimage12.png","url":"https://assets-eu.researchsquare.com/files/rs-7083063/v1/ef7d5e63fc9ed9a70bfdb71b.png"},{"id":94687394,"identity":"7afabf10-bd47-4dbf-b4d0-9aef9da5c61a","added_by":"auto","created_at":"2025-10-29 15:47:47","extension":"png","order_by":23,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":30032,"visible":true,"origin":"","legend":"","description":"","filename":"Onlinefloatimage13.png","url":"https://assets-eu.researchsquare.com/files/rs-7083063/v1/db6d633453ea8682cb7ea04f.png"},{"id":94687385,"identity":"effdcb72-e1ce-4d25-8fdb-43fb7765a58f","added_by":"auto","created_at":"2025-10-29 15:47:46","extension":"png","order_by":24,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":92668,"visible":true,"origin":"","legend":"","description":"","filename":"Onlinefloatimage2.png","url":"https://assets-eu.researchsquare.com/files/rs-7083063/v1/6d90b33828f09696215d4f3b.png"},{"id":94687396,"identity":"80f5afe0-db22-456a-ae2b-083131d7a1ea","added_by":"auto","created_at":"2025-10-29 15:47:47","extension":"png","order_by":25,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":292081,"visible":true,"origin":"","legend":"","description":"","filename":"Onlinefloatimage3.png","url":"https://assets-eu.researchsquare.com/files/rs-7083063/v1/362618b1e5e89c4d41c77900.png"},{"id":94687384,"identity":"26d70c1d-eb08-47ef-988b-66348a7873be","added_by":"auto","created_at":"2025-10-29 15:47:46","extension":"png","order_by":26,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":54111,"visible":true,"origin":"","legend":"","description":"","filename":"Onlinefloatimage4.png","url":"https://assets-eu.researchsquare.com/files/rs-7083063/v1/149f8393b481d602b9816954.png"},{"id":94687383,"identity":"d680bdea-98ae-4f75-84e0-48105af8c89a","added_by":"auto","created_at":"2025-10-29 15:47:46","extension":"png","order_by":27,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":17453,"visible":true,"origin":"","legend":"","description":"","filename":"Onlinefloatimage5.png","url":"https://assets-eu.researchsquare.com/files/rs-7083063/v1/fb2699ad4a86fc79e586001e.png"},{"id":94687390,"identity":"5b17a214-144d-4060-ad28-1e26bd02620f","added_by":"auto","created_at":"2025-10-29 15:47:47","extension":"png","order_by":28,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":16550,"visible":true,"origin":"","legend":"","description":"","filename":"Onlinefloatimage6.png","url":"https://assets-eu.researchsquare.com/files/rs-7083063/v1/2ee132e76bfcf8288a497557.png"},{"id":94728315,"identity":"0089beea-98df-4f0e-a114-81225e90366f","added_by":"auto","created_at":"2025-10-30 07:03:30","extension":"png","order_by":29,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":16296,"visible":true,"origin":"","legend":"","description":"","filename":"Onlinefloatimage7.png","url":"https://assets-eu.researchsquare.com/files/rs-7083063/v1/fe57344194ea63e74acff77f.png"},{"id":94687386,"identity":"f60c6339-04ea-4a33-8757-0d5c5d1916be","added_by":"auto","created_at":"2025-10-29 15:47:46","extension":"png","order_by":30,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":14783,"visible":true,"origin":"","legend":"","description":"","filename":"Onlinefloatimage8.png","url":"https://assets-eu.researchsquare.com/files/rs-7083063/v1/534ecbb5433505e6d6e9f4a4.png"},{"id":94728094,"identity":"06dc8bb9-398e-44fe-9fab-cc566ba21de2","added_by":"auto","created_at":"2025-10-30 07:03:04","extension":"png","order_by":31,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":23983,"visible":true,"origin":"","legend":"","description":"","filename":"Onlinefloatimage9.png","url":"https://assets-eu.researchsquare.com/files/rs-7083063/v1/235bac674a10065700674648.png"},{"id":94729248,"identity":"cc7f0628-1212-4ee1-aa9b-2fb83bafcb7c","added_by":"auto","created_at":"2025-10-30 07:04:43","extension":"xml","order_by":32,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":180933,"visible":true,"origin":"","legend":"","description":"","filename":"33b2ee26461642978f67c83cb6d34ecf1structuring.xml","url":"https://assets-eu.researchsquare.com/files/rs-7083063/v1/92451caedb1de4efa01e0e68.xml"},{"id":94687389,"identity":"27318652-a2cd-462d-92ed-6193fb4777ed","added_by":"auto","created_at":"2025-10-29 15:47:46","extension":"html","order_by":33,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":191852,"visible":true,"origin":"","legend":"","description":"","filename":"earlyproof.html","url":"https://assets-eu.researchsquare.com/files/rs-7083063/v1/f1adfc892da68e2b36f8a6cd.html"},{"id":94687352,"identity":"e3bb3a90-9a07-47bd-9680-4aadbed149d0","added_by":"auto","created_at":"2025-10-29 15:47:46","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":298878,"visible":true,"origin":"","legend":"\u003cp\u003eIllustration of adapted CT set up. Far left – appearance of CT with affixed close-focus lens (black circle over lens); middle – back brace where camera rested inside the tunnel; right – example set-up in the field with tunnel locked to a tree\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-7083063/v1/aef5a99cd35fa64a420b6c4d.png"},{"id":94687359,"identity":"85157a58-142a-4696-879c-24814e74f4e6","added_by":"auto","created_at":"2025-10-29 15:47:46","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":612221,"visible":true,"origin":"","legend":"\u003cp\u003eOwl pellet detections of GWTS (top) with positive detections indicated by red \u0026nbsp;\u0026nbsp;diamonds. CT sites (blue circles) within the area (red box) of positive owl \u0026nbsp;\u0026nbsp;pellet detections (bottom)\u003c/p\u003e","description":"","filename":"2.png","url":"https://assets-eu.researchsquare.com/files/rs-7083063/v1/23b2e8f5ddad990bebf8ecd2.png"},{"id":94687355,"identity":"912a2118-2656-499b-9e0d-da3e92910a98","added_by":"auto","created_at":"2025-10-29 15:47:46","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":383145,"visible":true,"origin":"","legend":"\u003cp\u003eSmall mammal species observed during CT survey. Top left- wood mouse and bank vole, \u0026nbsp;\u0026nbsp;top middle – bank vole, top-right – field vole, middle right – water shrew, \u0026nbsp;\u0026nbsp;bottom right – common shrew and bank vole, bottom middle – GWTS, bottom left \u0026nbsp;\u0026nbsp;– wood mouse, middle left – common shrew, centre – pygmy shrew\u003c/p\u003e","description":"","filename":"3.png","url":"https://assets-eu.researchsquare.com/files/rs-7083063/v1/930f34eb0eabc011186307aa.png"},{"id":94687397,"identity":"d21eedc8-bd20-421a-b3e0-8f36bb98574b","added_by":"auto","created_at":"2025-10-29 15:47:47","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":109604,"visible":true,"origin":"","legend":"\u003cp\u003eWood mouse detection probability by bait treatment over time. Bold line indicates model predictions, and dashed lines represent 95% confidence intervals\u003c/p\u003e","description":"","filename":"4.png","url":"https://assets-eu.researchsquare.com/files/rs-7083063/v1/68db6076b9c7256b7c65d463.png"},{"id":94728784,"identity":"3c4f69ce-2b5c-4fbd-9440-5b4f565fa9f1","added_by":"auto","created_at":"2025-10-30 07:04:16","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":121101,"visible":true,"origin":"","legend":"\u003cp\u003eModel estimates of the effect of time since bait on the detection probability of small mammal species. Shaded area indicates 95% confidence intervals based on model predictions\u003c/p\u003e","description":"","filename":"5.png","url":"https://assets-eu.researchsquare.com/files/rs-7083063/v1/5c87d4479d232d8d12bfb061.png"},{"id":94687358,"identity":"14005408-5aa8-466f-9663-1e9f5dd53f51","added_by":"auto","created_at":"2025-10-29 15:47:46","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":27883,"visible":true,"origin":"","legend":"\u003cp\u003eModel estimates of occupancy probability of common shrew and field vole in various habitats. Error bars indicate 95% confidence intervals\u003c/p\u003e","description":"","filename":"6.png","url":"https://assets-eu.researchsquare.com/files/rs-7083063/v1/bb5a929d84feffdf70d3929d.png"},{"id":94687366,"identity":"7ad546d3-c586-48b2-a28b-b92b7c7da426","added_by":"auto","created_at":"2025-10-29 15:47:46","extension":"png","order_by":7,"title":"Figure 7","display":"","copyAsset":false,"role":"figure","size":35796,"visible":true,"origin":"","legend":"\u003cp\u003eBoxplot of the number of captures in the 72hrs following bait treatment per deployment. Black cross – mean, horizontal bold line – median, box – interquartile range (IQR), whiskers – extend to smallest and largest values within 1.5*IQR distance from 25\u003csup\u003eth\u003c/sup\u003e and 75\u003csup\u003eth\u003c/sup\u003e percentile, respectively, dots - outliers\u003c/p\u003e","description":"","filename":"7.png","url":"https://assets-eu.researchsquare.com/files/rs-7083063/v1/31256b0975cb9b438fdc6a3e.png"},{"id":94687361,"identity":"9ffd60df-e92d-436e-b0d1-d80bc1fec014","added_by":"auto","created_at":"2025-10-29 15:47:46","extension":"png","order_by":8,"title":"Figure 8","display":"","copyAsset":false,"role":"figure","size":102130,"visible":true,"origin":"","legend":"\u003cp\u003eBoxplots illustrating the number of captures in the first 72 hours since bait treatment for small mammal species. Black cross – mean, horizontal bold line – median, box – interquartile range (IQR), whiskers – extend to smallest and largest values within 1.5*IQR distance from 25\u003csup\u003eth\u003c/sup\u003e and 75\u003csup\u003eth\u003c/sup\u003e percentile, respectively, dots - outliers\u003c/p\u003e","description":"","filename":"8.png","url":"https://assets-eu.researchsquare.com/files/rs-7083063/v1/936f47382b4bb296e20695ea.png"},{"id":94728846,"identity":"da4b5235-24ad-4884-a331-d35a613c0d1a","added_by":"auto","created_at":"2025-10-30 07:04:19","extension":"png","order_by":9,"title":"Figure 9","display":"","copyAsset":false,"role":"figure","size":67153,"visible":true,"origin":"","legend":"\u003cp\u003eLog-log plot comparing total captures from CTs during the present survey to observations in County Durham recorded in the NBN Atlas between 2015-2024. (NBN Atlas occurrence download at \u003ca href=\"https://nbnatlas.org/\"\u003ehttps://nbnatlas.org\u003c/a\u003e accessed on 01 July 2025)\u003c/p\u003e","description":"","filename":"9.png","url":"https://assets-eu.researchsquare.com/files/rs-7083063/v1/24e422e73f864029f62da591.png"},{"id":101690712,"identity":"4995f23e-7f9f-4e7a-affe-335ef5b4ef7a","added_by":"auto","created_at":"2026-02-02 16:08:01","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":2661437,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7083063/v1/500b842c-0fcd-4678-8e7e-f3f5a46e0830.pdf"},{"id":94687362,"identity":"cfc93e74-e5a1-4ea8-b1e3-e2d6ec3475d2","added_by":"auto","created_at":"2025-10-29 15:47:46","extension":"csv","order_by":0,"title":"","display":"","copyAsset":false,"role":"supplement","size":2325763,"visible":true,"origin":"","legend":"","description":"","filename":"Supplementary1.csv","url":"https://assets-eu.researchsquare.com/files/rs-7083063/v1/87e9aea1c8d338e0835fa20b.csv"},{"id":94687353,"identity":"4325136f-993f-43a8-b961-d359ae8f5089","added_by":"auto","created_at":"2025-10-29 15:47:46","extension":"xlsx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":14755,"visible":true,"origin":"","legend":"","description":"","filename":"Supplementary2.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-7083063/v1/73eb81dedb2acd1c5748d9d5.xlsx"},{"id":94728877,"identity":"abdd69dd-876f-46e3-8272-4206bb2dad1d","added_by":"auto","created_at":"2025-10-30 07:04:19","extension":"docx","order_by":2,"title":"","display":"","copyAsset":false,"role":"supplement","size":36290,"visible":true,"origin":"","legend":"","description":"","filename":"Supplementarymaterial3.docx","url":"https://assets-eu.researchsquare.com/files/rs-7083063/v1/9b3e55d6575a690522366aa5.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Camera trapping for small mammals: the case of a non- native shrew","fulltext":[{"header":"Introduction","content":"\u003cp\u003eIn Britain, as in the rest of the world, declines in species abundance have proceeded at an alarming rate over the past 50 years. Driven largely by agricultural land use changes and climate change, hundreds of species have experienced abundance declines in the UK, and almost 1500 native species are threatened with extinction, resulting in the UK being considered a nature-depleted nation (Burns et al., \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). Halting and reversing such declines in native species has become a legally binding target of the UK government, following the implementation of the Environment Act, which will require targeted management interventions (DEFRA, \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). As well as taxonomically widespread abundance declines in the UK, there have been accompanying distribution changes for native species. One group where such a change has been notably drastic is small mammals (here defined as mouse, vole, and shrew species). Among mammals that have experienced average abundance declines of 7% since 1970 (Burns et al., \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e2023\u003c/span\u003e), small mammals have experienced the largest percentage decrease in distribution compared to other assemblages (deer, bats, or mid-sized mammals), owing to declines in five of seven species analysed: bank vole, common shrew, field vole, harvest mouse, and water shrew (Coomber et al., \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2021\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eDue to their position as economically damaging pest species, small mammals are often the subjects of conflicting conservation and eradication agendas (Ferreira and Delibes-Mateos, \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2012\u003c/span\u003e). However, it is widely recognised that small mammals play vital roles in ecosystem functioning. Changes to small mammal communities can alter the structuring of vegetation (Hurst et al., \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e2014\u003c/span\u003e; Poe et al., \u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e2019\u003c/span\u003e); small rodents, in particular, play an essential role in seed dispersal and plant establishment processes (Godó et al., \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). As a key prey item, population cycles in small mammals can also drive numerical and functional responses in predators (Avotins et al., \u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2023\u003c/span\u003e), and can equally be driven by the density of predators (Korpimäki et al., \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e2005\u003c/span\u003e). Contractions in the distributions of the UK’s small mammals are troubling, therefore, not only because of the loss of small mammals, but also because of the wider environmental implications of such losses.\u003c/p\u003e\u003cp\u003eAssessing the state of nature is made possible by volunteer-based species monitoring schemes in the UK for birds, mammals, butterflies, moths, marine fish, and freshwater invertebrates (Burns et al., \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). Likely due to difficulties in distinguishing between small mammal species, they are typically the least recorded of mammals despite their large population sizes (Croft et al., \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2017\u003c/span\u003e). For small mammals, monitoring has historically relied on live trapping, a field methodology conducted by trained researchers and less accessible to the volunteer involvement that has granted large scale monitoring of other taxa. Physically capturing animals with this technique allows for unambiguous species identification as well as the retrieval of data concerning sex, age, and reproductive stage. Live trapping with capture-recapture methods enables inferences about the abundance of small mammals (Castañeda et al., \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2018\u003c/span\u003e), and, when traps are visited at frequent intervals throughout the day, daily activity patterns can be obtained (Kalinin and Aleksandrov, \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). However, live trapping is labour-intensive, as traps must be visited frequently to avoid mortality of captured animals. This limits the geographic and temporal scope of surveys and is demanding of human resources. Unfortunately mortality is a frequent result of live-trapping surveys, particularly for species with high metabolisms, such as shrews (Shonfield et al., \u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e2013\u003c/span\u003e; Stromgren and Sullivan, \u003cspan citationid=\"CR57\" class=\"CitationRef\"\u003e2014\u003c/span\u003e). Alternatively, small mammal presence can be non-invasively inferred using environmental DNA sampling (Verhees et al., \u003cspan citationid=\"CR61\" class=\"CitationRef\"\u003e2024\u003c/span\u003e) or track tubes where footprints are left by passing animals (Duffie et al., \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2019\u003c/span\u003e), but both techniques require a relatively high degree of researcher expertise; track tubes, in particular, involve a high degree of uncertainty in species assignments. For landscape-scale assessments, owl pellet dissection can provide estimates of small mammal community composition, but this technique permits only broad scale inferences as variations between and within local sites are disregarded and, due to owl habitat and dietary preferences, certain sites and species can be underrepresented (Heisler et al., \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e2016\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eFor larger body sized mammals, recent decades have seen a revolution in monitoring at scale due to the development of increasingly user-friendly and affordable camera trapping methods. By collecting observations of passing animals at any time of the day, motion-sensor cameras overcome challenges of observing nocturnal and elusive species, traits shared by both large and small mammals. Unlike other survey techniques, where animal encounters are often fleeting, images serve as records of the encounter and can be reviewed after the time of capture, facilitating the implementation of larger scale monitoring initiatives for larger body sized mammals (e.g. MammalWeb (Hsing et al., \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e2022\u003c/span\u003e), Snapshot Serengeti (Swanson et al., \u003cspan citationid=\"CR58\" class=\"CitationRef\"\u003e2015\u003c/span\u003e), or eMammal (McShea et al., \u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e2016\u003c/span\u003e)). Due to the cost-effectiveness of CTs relative to live trapping (De Bondi et al., \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2010\u003c/span\u003e), and their effectiveness at scaling up monitoring for larger bodied mammals, a recent burst of innovations has extended this method to small mammal surveys. Further, relative to live trapping, deducing activity patterns of captured animals is much simpler from CT records that are timestamped at the time of observation, facilitating investigations into interspecific activity patterns of small mammals (Probst and Probst, \u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). To best observe small mammals with CTs, it is necessary to reduce the field of view by confining CTs to tunnels, buckets, or boxes, and adding a close focus lens in front of the existing lens to reduce the focal distance. With these adaptations, CTs have been used to record high resolution images of mice, voles, shrews, small mustelids, and marsupials (Gracanin et al., \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; Littlewood et al., \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; McCleery et al., \u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e2014\u003c/span\u003e; Mos and Hofmeester, \u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Smaal and van Manen, \u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). Despite the success of these studies, adapting CTs for small mammals can be regarded as an approach in its infancy, yet to receive wide uptake. Furthermore, although previous efforts rely on bait or lures to increase detection probability, no rigorous consideration has been given to the potential for bait choice to influence the inferences made about the composition of small mammal communities. We address this knowledge gap in the present study by varying bait treatments between CTs and examining the effect on number of captures as well as detection probability of small mammal species. Adapted camera traps have been deployed in northern Scotland to successfully assess small mammal occurrence in different habitats (Littlewood et al., \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). As this survey considers the same target species as Littlewood et al. (\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e2021\u003c/span\u003e), their design was closely followed in the construction of adapted CTs for this project.\u003c/p\u003e\u003cp\u003eThe north of England has previously been characterised as a ‘data desert’ for British mammals owing to a relatively lower survey effort than other parts of the country (Croft et al., \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2017\u003c/span\u003e). By conducting our CT survey in this region, we aim to improve regional mammal records with high-resolution observations of small mammal species. Moreover, our application of adapted CTs to the north of England is incidentally relevant because of the detection of the non-native greater white-toothed shrew, \u003cem\u003eCrocidura russula\u003c/em\u003e (hereafter, GWTS). While DNA analysis confirmed the presence of the GWTS near Sunderland, in 2021, dated photographs indicate the species has been present in the area since 2015 (Bond et al., \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). The GWTS has demonstrated itself to be a formidable invasive species in Ireland over the past 20 years, with a radial expansion rate of approximately 5.5km a year since being first detected in 2008 (McDevitt et al., \u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e2014\u003c/span\u003e; Tosh et al., \u003cspan citationid=\"CR60\" class=\"CitationRef\"\u003e2008\u003c/span\u003e). Based on current reports from owl pellet dissection and live trapping in Britain, it appears that the GWTS is largely confined to the area between Durham and Sunderland in the North East of England (Smith et al., in review), in addition to a population in Nottinghamshire (McDevitt, \u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). The species occupies a wide range of habitats including farmland, hedgerows, forest, woodland, urban areas, and gardens. The competitive abilities of the GWTS are evident within its native habitat where it has been expanding its range at the expense of congenerics (Vogel et al., \u003cspan citationid=\"CR63\" class=\"CitationRef\"\u003e2002\u003c/span\u003e) as well as out-with its native range in Ireland, where it is implicated in the disappearance of native pygmy shrew \u003cem\u003eSorex minutus\u003c/em\u003e from invaded zones as well as observed negative effects on soil surface invertebrate communities (McDevitt et al., \u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e2014\u003c/span\u003e; Montgomery et al., \u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). Based on their well-documented invasion in Ireland, a rapid risk assessment recommended a dedicated monitoring effort to elucidate potential impacts on native small mammals in Britain, particularly the seemingly vulnerable pygmy shrew (McDevitt, \u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). We therefore chose to conduct our trial of adapted small mammals CTs in areas where the non-native species has been detected, contributing to knowledge on the status of native species in ‘invaded’ zones.\u003c/p\u003e\u003cp\u003eTo promote further application of adapted CTs, we assess differences in the rate of capture of small mammals by various bait treatments to offer insight into deployment strategies for adapted CTs. A combination of peanut butter and oats is regarded as a universal bait in small mammal surveys, and has generally has high success at attracting small mammals indiscriminately (Kok et al., \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e2013\u003c/span\u003e; Paull et al., \u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e2011\u003c/span\u003e). However, it seems unlikely that one bait has universal appeal to small mammals with different dietary preferences. Live trapping studies have demonstrated that including mealworms in bait treatments reduces the accidental mortality of captured shrew species (Do et al., \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2013\u003c/span\u003e; Stromgren and Sullivan, \u003cspan citationid=\"CR57\" class=\"CitationRef\"\u003e2014\u003c/span\u003e), but little consideration has been given to the potential bias involved in selecting one bait type over another. We predict that including mealworms in bait treatments will improve the detection of shrew species, since this more closely resembles their typical insect food source than grain-based baits, and that all baits will lead to higher capture rates for all species than un-baited controls.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003e\u003c/p\u003e"},{"header":"Methods","content":"\u003cp\u003e\u003cb\u003eAdapted camera traps and technical settings\u003c/b\u003e\u003c/p\u003e\u003cp\u003eWe constructed Littlewood boxes based on Littlewood et al (\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e2021\u003c/span\u003e) and used Browning Recon Force Elite HP5 Camera Traps (Browning Trail Cameras, USA). Two prototype tunnels were compared in a pilot study to support the final design of tunnels used in this analysis. The pilot tested a box open at one end against a tunnel with openings at both ends. Animals made use of both entrances where the tunnel was open at both ends and, due to the added advantage of allowing small mammals to escape potential predators if cornered in boxes, we opted for a tunnel design with two open ends, departing slightly from the design of Littlewood et al (\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). Each tunnel was constructed using three 60cm x 20cm x2.5cm wooden sides and a 60cm x 20cm Perspex roof. The Perspex roof, introduced by Littlewood et al. (\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e2021\u003c/span\u003e), ensures more light is available to the CT within the tunnel, permitting colour images to be taken during the day and facilitating improved species identification. Placing CTs inside one end of the tunnel removes the requirement for weatherproofing necessary when cameras are outside of the tunnel as in Littlewood et al. (\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). The tunnels were slightly longer than that employed by Littlewood et al (\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e2021\u003c/span\u003e) to compensate for the cameras being inside the box, thus maintaining the camera’s field of view relative to the Littlewood box. At one end of each tunnel, we drilled holes into each side approximately 7cm from the tunnel floor to accommodate a python lock that threaded through the CT’s back brace, allowing the CT to be suspended in the air. At the same end of the tunnel, we added braces that permitted us to tilt the camera toward a bait station (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). The python lock passed through the camera and tunnel, and could be secured to a tree, log, or fencepost at the survey site. The specifications of our tunnels catered to the specific CTs used; surveys using alternative camera models should adjust measurements accordingly.\u003c/p\u003e\u003cp\u003eCamera traps were programmed to record bursts of 3 photos in a sequence per trigger of the motion-sensor, with a 1-minute delay between triggers to balance the desire to obtain sufficient footage while avoiding excessive activity that would fill up SD cards and deplete battery life. The IR flash power was set to ‘economy’. Although Littlewood et al. (\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e2021\u003c/span\u003e) found it necessary to cover the infra-red flash of the cameras used in their survey, in a pilot study we found our cameras did not require this adaptation, owing to the “illuma-smart” technology that overcomes problems of overexposure of close-up animals. In front of the existing camera lens, we affixed a close-focus + 4 macro camera filter (37mm diameter) with blu-tac (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). Eight rechargeable batteries powered each camera trap and were replenished as required.\u003c/p\u003e\u003cp\u003e\u003cb\u003eCamera deployment\u003c/b\u003e\u003c/p\u003e\u003cp\u003eSurvey sites were selected within the GWTS’s known range in the northeast of England, based on presence/absence surveys of owl pellets by local researchers (Ian Bond and Terry Coult) (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). Currently, evidence suggests that GWTS range (excluding the Nottinghamshire population) is constrained between Sunderland and Durham. During the fieldwork period, a specimen from Peterlee was identified after capture by a domestic cat belonging to a Durham University colleague, resulting in the decision to survey a further site (Castle Eden Dene), which was not within the boundaries suggested by the owl pellet survey. Chosen sites depended on survey permissions but, in total, eight sites were surveyed between May and October 2024: Durham Botanic Gardens, Rainton Meadows, Herrington Country Park, Tunstall Hills, Hetton Lyons Country Park, Castle Eden Dene, Sherburn Village and Sherburn Grange Farm (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). Two habitats were surveyed with 12 cameras each at Rainton Meadows: grassland and woodland. Apart from this case at Rainton Meadows there were only 12 unique camera locations at each site.\u003c/p\u003e\u003cp\u003eWithin each site, we selected locations away from areas of major public access and with appropriate structures (e.g., fence post, tree or log) to which to lock CT tunnels. Within each cluster, each CT was spaced approximately 3–6 metres apart from its nearest neighbour depending on structures available to attach the lock. This dense placement was intended to ensure that all traps within a cluster were within an area that reflects a small mammal home range. Three clusters were deployed simultaneously at each survey site so that, during a deployment, twelve adapted CTs were active in three distinct clusters. We aimed to maximise heterogeneity of cluster placements within sites so, at the largest site, Castle Eden Dene, the clusters were up to 1.25 kilometres apart; at the smallest site, Sherburn Farm, clusters were as little as 50 metres apart.\u003c/p\u003e\u003cp\u003e\u003cb\u003eBait treatment\u003c/b\u003e\u003c/p\u003e\u003cp\u003eAluminium weighing boats (56mm diameter) were used as bait trays in the CT tunnels, fixed in place with drawing pins. Four bait treatments were tested: (1) peanut butter and oats, (2) dried mealworms, (3) a mixture of 1 and 2, and (4) a control treatment with an empty bait tray. Camera traps were deployed in groups of 4, with each tunnel corresponding to one of the four bait treatments. Each group of 4 is referred to as a ‘cluster’. Tunnels received the same bait treatment throughout the survey to control for any effect of scent that may linger in boxes and act as a lure. Bait was replenished every 3–6 days, except for one period (21/05/2024–03/06/2024) where no bait was re-filled due to researcher unavailability.\u003c/p\u003e\u003cp\u003eOverall, cameras were deployed at 108 unique locations during the survey, corresponding to 27 clusters (3 clusters at each of 7 sites, and two groups of 3 clusters at a further site). Among the surveyed sites, farmland, woodland and grassland habitats were represented. Based on records from the National Biodiversity Network (NBN) Atlas, the small mammals (mice, voles, and shrews) recorded in this region since 2015 includes the shrew species pygmy shrew \u003cem\u003eS. minutus\u003c/em\u003e, common shrew \u003cem\u003eS. araneus\u003c/em\u003e, water shrew \u003cem\u003eN. fodiens\u003c/em\u003e, and GWTS, and the rodents: wood mouse \u003cem\u003eApodemus sylvaticus\u003c/em\u003e, yellow-necked mouse \u003cem\u003eA. flavicollis\u003c/em\u003e, house mouse \u003cem\u003eMus musculus\u003c/em\u003e, harvest mouse \u003cem\u003eMicromys minutus\u003c/em\u003e, fat dormouse \u003cem\u003eGlis glis\u003c/em\u003e, hazel dormouse \u003cem\u003eMuscardinus avellanarius\u003c/em\u003e, bank vole \u003cem\u003eMyodes glareolus\u003c/em\u003e, field vole \u003cem\u003eMicrotus agrestis\u003c/em\u003e, water vole \u003cem\u003eArvicola amphibus\u003c/em\u003e, and Orkney vole \u003cem\u003eMicrotus arvalis\u003c/em\u003e (NBN Trust, \u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e2025\u003c/span\u003e). As a publicly available data repository that collates records from many different sources nationally, the NBN is vulnerable to erroneous records of rare or unlikely species, so we note that there are some species unlikely to be present in this area (yellow-necked mouse, harvest mouse, fat dormouse, hazel dormouse, and Orkney vole).\u003c/p\u003e\u003cp\u003e\u003cb\u003eImage collection/processing\u003c/b\u003e\u003c/p\u003e\u003cp\u003eAt each re-baiting visit, images were copied in the field from SD cards onto a hard drive and subsequently uploaded to MammalWeb (Hsing et al., \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e2022\u003c/span\u003e) at the first available opportunity. MammalWeb offers a user-friendly portal for image classification, where bursts of images taken at each camera trigger are automatically presented to the classifier as a ‘sequence’ of images taken within 10 seconds of each other, for classification, accompanied by a list of plausible species based on the host project. Sequences from this fieldwork were uploaded to the ‘Shrew monitoring in Britain’ subproject within a larger ‘Small Mammal Camera Trapping’ project. Therefore, the accompanying list of species in the classification interface included small mammal species observable in the UK. In addition, MammalWeb passes uploaded images through Megadetector (Beery et al., \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2019\u003c/span\u003e) to screen out images that contain humans or are devoid of wildlife, minimising the time required to process images that do not contain target species. In a preliminary analysis, the accuracy of Megadetector was assessed over 6,771 image sequences that had received human classification by C.S. (lead author). Of sequences that Megadetector classifies as containing ‘Nothing’ (936 sequences), there were only 18 in which C.S. alternatively identified an animal. Therefore, for the dataset as a whole, we accepted with a high degree of confidence that there are unlikely to be animals missed in the images classed as ‘Nothing’ by Megadetector (~ 3500 sequences). Although the project was open to any volunteer contributor to MammalWeb, only classifications by C.S. are used in the current analysis.\u003c/p\u003e\u003ch2\u003eData analysis\u003c/h2\u003e\u003cp\u003eIf a camera had malfunctioned during a given deployment, data from other cameras within that cluster and deployment were excluded to equalise effort across bait treatments. Data from cameras in Sherburn Village were included, only up to the last data collection timepoint prior to the theft of 8 of the 12 cameras. During data processing, each capture was assigned a ‘time since bait’ that described the numeric time (hours) elapsed between the bait treatment being refilled and the timestamp on the CT sequence, allowing us to investigate the effect of re-baiting frequency on small mammal detections.\u003c/p\u003e\u003cp\u003eSingle season occupancy models were used to examine the variables influencing occupancy and detection of small mammals at camera traps. This approach assumes that the presence or absence of a species at a site (occupancy) is constant throughout the survey period, that there are no false positive detections, and that detections are independent between sites (MacKenzie et al., \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e2002\u003c/span\u003e). Models were specified using the ‘occu’ function in the ‘unmarked’ package in R (Fiske and Chandler, \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e2011\u003c/span\u003e; Kellner et al., \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). The models were based on detection histories that, for each species, consisted of a matrix where rows correspond to CT sites and columns correspond to repeated survey periods at those sites which, in this case, were specified as 24-hour survey periods.\u003c/p\u003e\u003cp\u003eOccupancy models have two sub models: the first models the occupancy probability of a species as a function of site-level covariates, and the second models detection probability of that species as a function of site and survey level covariates. The global model allowed detection to vary according to bait type and time elapsed since bait, and occupancy to vary according to habitat type. This global model was fitted for wood mouse, bank vole, common shrew, pygmy shrew, and field vole, but for water shrew and the greater white-toothed shrew, a paucity of observations meant that only the null model (providing estimates for occupancy and detection probabilities without covariate influence) could be fitted with confidence. For species for which the global model could be fitted, all possible submodels were assessed using the ‘dredge’ function in the ‘MuMIn’ package within R (Bartoń, \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). To inform the predictions reported here, all models within 6 ΔAIC\u003csub\u003ec\u003c/sub\u003e of the best model were considered, excluding models with additional predictors relative to the best model (Richards et al., \u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e2011\u003c/span\u003e). In the case that there was more than one best model according to these specifications, the reported predictions here came from averaging the model predictions according to their AICc-based weight using the modavgPred function in the AICcmodavg package (Mazerolle, \u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). For each species’ best model, model fit was evaluated with a parametric bootstrap goodness-of-fit test (MacKenzie and Bailey, \u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e2004\u003c/span\u003e) using 1,000 iterations. The parametric bootstrap enhances the utility of the occupancy models by assessing whether they provide a realistic representation of the data (p \u0026gt; 0.05), or if they display a significant lack of fit to the data (p \u0026lt; 0.05).\u003c/p\u003e\u003cp\u003eTo assess broad differences between bait types in the rate of capture, the number of captures at each camera in the first 72 hours of deployment were aggregated. This period was selected since 72 hours was the minimum deployment duration and so was comparable between all camera deployments. Data were not normally distributed, so non-parametric Kruskal-Wallis tests were used to assess differences in the number of captures per deployment between bait treatments, using a significance level of p \u0026lt; 0.05. This was first done for all species and subsequently for each species individually. Post-hoc Pairwise Wilcoxon rank-sum tests with Bonferroni correction were conducted to investigate significant relationships between the bait treatments regarding the mean number of captures.\u003c/p\u003e\u003cp\u003eTo provide inference regarding the impact of non-native GWTS, observational records from the National Biodiversity Network (NBN) Atlas for County Durham (NBN Trust, \u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e2025\u003c/span\u003e) were downloaded for each of the small mammal species observed on CT images. This download is included as supplementary material (Supplementary 1), as well as details of the data providers (Supplementary 2). Records included in the NBN database come from a diverse range of sources, such as live trapping studies as well as roadkill observations or ad hoc sightings, and therefore the records were considered a suitable source against which we compared the relative frequencies of observation of the species captured in our CTs. The total number of records for each species between 2015–2024 in County Durham was plotted on a log scale against the total number of captures from the CT survey, also on a log scale, to compare relative frequencies of observation.\u003c/p\u003e"},{"header":"Results","content":"\u003cp\u003eAcross all CTs, 50,328 sequences were captured and uploaded to MammalWeb. After passing through Megadetector, 46,824 sequences were deemed likely (Pr \u0026gt; 0.1) to contain an animal and were classified by C.S. A total of 27 species were identified, and for sequences where an animal could not be identified to the species level, the label assigned was one of the following: vole (unknown species), unidentified bird, small rodent (unknown species), shrew sp, other, nothing, human, or don\u0026rsquo;t know. After excluding sequences from partial deployments and where the individual could not be identified to species-level, 30,368 small mammal-containing sequences were included in this analysis, with 27 sequences that included more than one species (Table 1). \u0026nbsp;These data, excluding clusters where there were malfunctions, corresponded to 2,161 trap days (24-hour periods). Seven small mammal (mice, vole, or shrew) species were identified, with body size, ear size, colouration, and tail length and width being features that facilitated classification from the high-resolution imagery obtained (Figure 3). Wood mouse, bank vole, common shrew, and pygmy shrew were detected at all survey sites (Table 2). \u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 1.\u003c/strong\u003e Total number of CT sequences in which small mammal species were observed, as well as the number used in occupancy analyses after excluding sequences from partial deployments.\u0026nbsp;\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eSpecies\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eTotal number of sequences\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eAnalysis sequences\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eWood mouse\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e16123\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e15474\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eBank vole\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e11838\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e11601\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003ePygmy shrew\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e1863\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e1700\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eCommon shrew\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e1404\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e1366\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eField vole\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e229\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e223\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eGWTS\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e48\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e27\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eWater shrew\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e4\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eSmall rodent (unknown species)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e1147\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eVole (unknown species)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e322\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eShrew (unknown species)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e279\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003cimg src=\"data:image/png;base64,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\"\u003e\u003c/p\u003e\n\u003cp\u003eOccupancy modelling\u003c/p\u003e\n\u003cp\u003eFor wood mouse, bank vole, and pygmy shrew, the model selection process resulted in a single best model being used to provide the inferences presented for each, but for field vole and common shrew there were two candidate models over which model averaging was used to generate predictions (Supplementary material 3). Detection probabilities for all species\u003cem\u003e\u0026nbsp;\u003c/em\u003ewere significantly affected by time since bait (Table 3). There was a significant negative correlation between the time elapsed since bait and the detection probability, although the magnitude of this differed by species (Figures 4 and 5). Parametric bootstrap goodness of fit tests for each model indicated no significant lack of fit (p \u0026gt; 0.05, Table 3), suggesting that the best model(s) fit the data well for each species. In the first 24 hours following bait treatment, the detection probability was highest for wood mouse (Figure 4), and lowest for field vole (Table 4 and Figure 5). Habitat type had no significant effect on the occupancy of wood mouse, bank vole, or pygmy shrew, but common shrew and field vole were less likely to occupy woodland than farmland and grassland (Figure 6 and Table 4). For wood mouse, there was a statistically significant interaction between bait type and time since bait, with a higher detection probability maintained over time for the peanut butter and oats bait compared to all other treatments (Figure 4). The null model for water shrew predicted an occupancy of 0.009 (0.001-0.06) and detection probability of 0.59 (0.19-0.90), with the model providing a good fit to the data (chi-square statistic = 537, bootstrapped p-value = 0.59). For the GWTS\u003cem\u003e,\u0026nbsp;\u003c/em\u003eoccupancy was predicted to be 0.09 (0.04-0.19) with a corresponding detection probability of and 0.24 (0.11-0.45), again with a good fit to the data (chi-square statistic = 517, bootstrapped p-value = 0.50). GWTS were recorded at cameras with MW, MW + PB, and PB treatments, while the water shrew was recorded at only one camera with a MW treatment.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eTable 3. Description of predictors included in the best model for each species. Measures of model fit included are AICc-based model weight, the chi-square statistic and bootstrap p-value from parametric bootstrap goodness of fit tests (p \u0026lt; 0.05 indicates significant lack of model fit). The effect size of the continuous variable \u0026lsquo;days since bait\u0026rsquo; is included, as this was a significant detection predictor for every species. \u003cstrong\u003e\u003cbr\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 120px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 70px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eOccupancy\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"3\" valign=\"top\" style=\"width: 200px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eDetection\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" valign=\"top\" style=\"width: 70px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eAICc-based model weight\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" valign=\"top\" style=\"width: 73px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eChi-square test statistic\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" valign=\"top\" style=\"width: 69px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eBootstrap p-value\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 120px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eSpecies\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 70px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eHabitat\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 61px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eBait type\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 60px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eDays since bait\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 79px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eBait type*days since bait\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 120px;\"\u003e\n \u003cp\u003eWood mouse\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 70px;\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 61px;\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 60px;\"\u003e\n \u003cp\u003e-1.12\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 79px;\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 70px;\"\u003e\n \u003cp\u003e0.81\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 73px;\"\u003e\n \u003cp\u003e122\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 69px;\"\u003e\n \u003cp\u003e0.73\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 120px;\"\u003e\n \u003cp\u003eBank vole\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 70px;\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 61px;\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 60px;\"\u003e\n \u003cp\u003e-0.60\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 79px;\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 70px;\"\u003e\n \u003cp\u003e0.59\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 73px;\"\u003e\n \u003cp\u003e191\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 69px;\"\u003e\n \u003cp\u003e0.52\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 120px;\"\u003e\n \u003cp\u003ePygmy shrew\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 70px;\"\u003e\n \u003cp\u003eNo\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 61px;\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 60px;\"\u003e\n \u003cp\u003e-0.28\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 79px;\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 70px;\"\u003e\n \u003cp\u003e0.69\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 73px;\"\u003e\n \u003cp\u003e451\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 69px;\"\u003e\n \u003cp\u003e0.56\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"2\" valign=\"top\" style=\"width: 120px;\"\u003e\n \u003cp\u003eCommon shrew\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 70px;\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 61px;\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 60px;\"\u003e\n \u003cp\u003e-0.58\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 79px;\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 70px;\"\u003e\n \u003cp\u003e0.85\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 73px;\"\u003e\n \u003cp\u003e387\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 69px;\"\u003e\n \u003cp\u003e0.56\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 70px;\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 61px;\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 60px;\"\u003e\n \u003cp\u003e-0.58\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 79px;\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 70px;\"\u003e\n \u003cp\u003e0.07\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 73px;\"\u003e\n \u003cp\u003e386\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 69px;\"\u003e\n \u003cp\u003e0.53\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"2\" valign=\"top\" style=\"width: 120px;\"\u003e\n \u003cp\u003eField vole\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 70px;\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 61px;\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 60px;\"\u003e\n \u003cp\u003e-0.40\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 79px;\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 70px;\"\u003e\n \u003cp\u003e0.74\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 73px;\"\u003e\n \u003cp\u003e528\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 69px;\"\u003e\n \u003cp\u003e0.59\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 70px;\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 61px;\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 60px;\"\u003e\n \u003cp\u003e-0.40\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 79px;\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 70px;\"\u003e\n \u003cp\u003e0.21\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 73px;\"\u003e\n \u003cp\u003e506\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 69px;\"\u003e\n \u003cp\u003e0.55\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u0026nbsp;Table 4. Estimates of occupancy and detection probability from the best model(s) for small mammal species, with 95% confidence intervals in brackets.\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"2\" valign=\"top\" style=\"width: 123px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eSpecies\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"3\" valign=\"top\" style=\"width: 366px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eOccupancy probability\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" valign=\"top\" style=\"width: 112px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eDetection probability in first 24 hours since bait\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 132px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eWoodland\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 121px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eGrassland\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 112px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eFarmland\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 123px;\"\u003e\n \u003cp\u003eWood mouse\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 132px;\"\u003e\n \u003cp\u003e0.98 (0.93-0.995)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 121px;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 112px;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 112px;\"\u003e\n \u003cp\u003eFigure 4\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 123px;\"\u003e\n \u003cp\u003eBank vole\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 132px;\"\u003e\n \u003cp\u003e0.83 (0.75-0.89)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 121px;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 112px;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 112px;\"\u003e\n \u003cp\u003e0.95 (0.91-0.97)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 123px;\"\u003e\n \u003cp\u003ePygmy shrew\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 132px;\"\u003e\n \u003cp\u003e0.55 (0.46-0.65)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 121px;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 112px;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 112px;\"\u003e\n \u003cp\u003e0.62 (0.53-0.71)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 123px;\"\u003e\n \u003cp\u003eCommon shrew\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 132px;\"\u003e\n \u003cp\u003e0.39 (0.29-0.51)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 121px;\"\u003e\n \u003cp\u003e0.84 (0.52-0.96)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 112px;\"\u003e\n \u003cp\u003e0.63 (0.42-0.80)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 112px;\"\u003e\n \u003cp\u003e0.83 (0.75-0.89)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 123px;\"\u003e\n \u003cp\u003eField vole\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 132px;\"\u003e\n \u003cp\u003e0.27 (0.16-0.42)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 121px;\"\u003e\n \u003cp\u003e0.59 (0.21-0.88)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 112px;\"\u003e\n \u003cp\u003e0.41 (0.22-0.64)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 112px;\"\u003e\n \u003cp\u003e0.51 (0.36-0.65)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eNumber of captures\u003c/p\u003e\n\u003cp\u003eAcross all species and sites surveyed, the number of captures differed significantly between bait treatments (H\u003csub\u003e(3)\u0026nbsp;\u003c/sub\u003e= 174.33, p \u0026lt; 0.05). The number of captures for cameras with no bait was significantly lower than for cameras with PB (p \u0026lt; 0.05), MW (p \u0026lt; 0.05) or PB+MW (p \u0026lt; 0.05); however, across bait types, there were no significant differences (p = 1) (Figure 7). The significantly lower capture rate in un-baited traps was also recorded for individual species: wood mouse\u0026nbsp;(H\u003csub\u003e(3)\u003c/sub\u003e = 125.52, p \u0026lt; 0.05), bank vole (H\u003csub\u003e(3)\u0026nbsp;\u003c/sub\u003e= 78.19, p \u0026lt; 0.05), common shrew (H\u003csub\u003e(3)\u003c/sub\u003e = 34.19, p \u0026lt; 0.05) and pygmy shrew (H\u003csub\u003e(3)\u0026nbsp;\u003c/sub\u003e= 26.29, p \u0026lt; 0.05) (Figure 8). Captures of water shrew were isolated to one CT which had a MW treatment, so additional statistical analyses were not informative. For field vole, there was no significant difference between any bait treatment (H\u003csub\u003e(3)\u0026nbsp;\u003c/sub\u003e= 4.16, p = 0.25), as was the case for the GWTS (H\u003csub\u003e(3)\u0026nbsp;\u003c/sub\u003e= 2.67, p = 0.44). For all shrew species observed, the mean number of captures was greatest at the MW bait treatment (Figure 8).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eRelative capture rates of GWTS and native small mammals\u003c/p\u003e\n\u003cp\u003eFrequency of observation of native small mammals using\u003cem\u003e\u0026nbsp;\u003c/em\u003eadapted CTs correlated positively with frequency of reports of those species in County Durham via NBN (Figure 9). However, the GWTS was documented in our CT survey more frequently than would be predicted based on the number of reports in the NBN database. Further, we did not identify the following species in any CT sequences: water vole, house mouse, harvest mouse, Orkney vole, hazel dormouse, yellow-necked mouse or fat dormouse although they have been reported at various frequencies in the region via the NBN atlas.\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eWe trialled a relatively new method of surveying small mammals, partly motivated by the urgent need for appraisal of the UK\u0026rsquo;s native small mammals in the presence of the non-native GWTS, but more directly motivated by a requirement to innovate new methods for scaling up data collection on small mammals that have not been historically well recorded in the UK. For the species observed - wood mouse, bank vole, field vole, common shrew, pygmy shrew, water shrew and GWTS - we were able to deduce the proportion of sites occupied, and to investigate the effect of different bait treatments on detection probabilities and number of captures. Although using bait significantly increases the number of captures, bait type had no significant effect on the probability of detection for any species. Apart from the water shrew, a specialist of wetland habitats that were not represented in this survey, the GWTS had the lowest predicted occupancy of all small mammal species observed. We discuss our findings in relation to the success of the adapted CT methodology, implications of our bait treatment experiment for informing the optimal use of adapted CTs and, finally, what the results of our survey suggest about the potential GWTS invasion in the UK.\u003c/p\u003e\u003cp\u003e\u003cb\u003eAdapting camera traps for small mammals\u003c/b\u003e\u003c/p\u003e\u003cp\u003eThe adapted CT design trialled in this survey permitted observation of small mammals and reliably captured species that we expected to be present. The quality of images obtained during our survey rival the current best methods (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e) and provide a method superior to techniques where cameras are placed on poles and pointed at bare patches of ground. Although pole-mounted cameras can facilitate capture and identification of rodents and marsupials ranging from dunnarts to kangaroos (De Bondi et al., \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2010\u003c/span\u003e; Rendall et al., \u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e2014\u003c/span\u003e), it would be difficult to distinguish confidently between smaller mammal species, such as shrews, photographed this way. Additionally, the pole-mounting method is not as feasible in areas of denser ground vegetation (Littlewood et al., \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). Recently, pole mounted cameras that are lower to the ground (50cm), have a close focus lens and were tilted toward bait stations have proved successful for monitoring small mammal communities in Germany (Rimbach et al., \u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e2025\u003c/span\u003e). Downward facing cameras in buckets have better facilitated distinction between voles, mice, and rats (Dueser et al., \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e2025\u003c/span\u003e; McCleery et al., \u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e2014\u003c/span\u003e), and downward facing cameras have been used within tunnels to distinguish between voles, shrews, and stoats beneath snow (Soininen et al., \u003cspan citationid=\"CR56\" class=\"CitationRef\"\u003e2015\u003c/span\u003e). This design has permitted year-round continuous monitoring of small mammals in Norway, providing insight on their seasonal and diel activity patterns (Linds\u0026oslash; et al., \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e2025\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eThe face-on design used here and in Littlewood et al. (\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e2021\u003c/span\u003e) better allows researchers to distinguish species within groups of small mammals (i.e., different species of mice, voles or shrews), a process that necessitates higher resolution of smaller physical features. Recent research has suggested that, with camera trap boxes, species identification is only possible for larger bodied small mammals (\u0026gt;\u0026thinsp;25cm in length) (Fink and Jachowski, \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2025\u003c/span\u003e); however, we concur with Littlewood et al. (\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e2021\u003c/span\u003e) that boxes provide a good method for small species discrimination within the UK small mammal assemblage. We added a bait station that encouraged animals to spend more time in the camera\u0026rsquo;s high focus area, as suggested by Littlewood et al. (\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). Since animals were frequently photographed in this zone, this likely aided the capacity for species distinctions to be made. Similarly high resolution images have been obtained for arboreal sugar gliders (\u003cem\u003ePetaurus breviceps\u003c/em\u003e) and brown antechinus (\u003cem\u003eAntechinus stuartii\u003c/em\u003e) using tunnels placed in trees (Gracanin et al., \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e2022\u003c/span\u003e), and PVC tunnels on the ground have permitted observation of multiple shrew, vole, and mouse species in the Netherlands (Smaal and van Manen, \u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). Our survey thus adds to a growing body of literature that demonstrates that only a few minor adjustments to conventional CTs can enable more efficient small mammal surveys with confident species identifications.\u003c/p\u003e\u003cp\u003eThe correlation between the frequency with which native species were observed during this survey and the frequency with which they have been reported in the last 10 years in County Durham via the NBN Atlas speaks to the potential of CTs to capture a sample of the small mammal population representative of that which would be observed by other means. This is encouraging for future uptake of the method for small mammal surveys, since there are several advantages relative to the convention of live trapping. Firstly, reduced field work time results in a more cost-effective strategy than live-trapping (De Bondi et al., \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2010\u003c/span\u003e; Villette et al., \u003cspan citationid=\"CR62\" class=\"CitationRef\"\u003e2016\u003c/span\u003e; Verhees et al., \u003cspan citationid=\"CR61\" class=\"CitationRef\"\u003e2024\u003c/span\u003e), providing a financial incentive for researchers. Secondly, as an \u0026lsquo;open\u0026rsquo; trap, CTs also better enable surveys to record multiple species in-between researcher visits, making them more efficient at species detection. More abundant species have a higher probability of encountering traps first and therefore could block live traps from recording rarer species, especially when cameras are programmed to have a delay between triggers. When rare species are of concern, as they often are in ecological assessments, CTs are therefore more likely to detect them. Further, there are ethical incentives for prioritising non-invasive survey methods to avoid disturbing animals. This is particularly relevant for shrews that have heightened vulnerability to mortality in live trapping surveys (Shonfield et al., \u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e2013\u003c/span\u003e). Given the climate of concern regarding native UK shrew species in the presence of the non-native GWTS, the avoidance of trap mortality represents a major advantage of CTs.\u003c/p\u003e\u003cp\u003eSeveral species reported in County Durham in the past decade were not recorded during this CT survey. Most of these species (yellow-necked mouse, harvest mouse, fat dormouse, hazel dormouse, Orkney vole) are unlikely or rarely encountered in County Durham and are most likely to be misidentifications. This raises an important feature of CT monitoring: images serve as vouchers of the encounter, so records can be better verified than by alternative survey methodologies. The water vole and house mouse are likely present in the region, but their non-detection is explained because they exist in more specialised habitats that were not represented in this survey.\u003c/p\u003e\u003cp\u003e\u003cb\u003eOptimising detection by adapted CTs\u003c/b\u003e\u003c/p\u003e\u003cp\u003eSeven species of small mammal were detected during this survey (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e) and as expected, CT stations with bait recorded significantly more captures per deployment of every species than CTs with no bait, irrespective of bait type. The mean number of captures for all shrew species (common shrew, pygmy shrew, water shrew, GWTS) was highest at CTs with the mealworm-only bait treatment. Given that including mealworms does not result in significantly lower captures of rodents\u0026rsquo; wood mouse, bank vole, or field vole (Fig.\u0026nbsp;6), mealworms seem to be a worthwhile inclusion in bait treatments. Time since baiting negatively affected the detection of all species so, for longer-term studies, it may be preferable to use an alternative baiting method that requires less frequent researcher visits. One solution could be to use a scent lure instead of bait, to extend attraction to traps temporally. Salmon oil performed this function in a survey investigating the optimal bait strategy for weasel detection with camera traps (Bergeson et al., \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2025\u003c/span\u003e), and automated scent dispersers at CTs have proven successful for minimising required researcher visits at sites with challenging and dangerous conditions when surveying rare carnivores (Long et al., \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). Both represent solutions for maintaining high detection probabilities while reducing the requirement for frequent researcher visits.\u003c/p\u003e\u003cp\u003eWhile baited traps recorded significantly more captures than unbaited traps, bait type had no significant effect on the probability of detection for any species. This contrasted with our expectations. If indeed non-baited traps could provide the same power of detection, this would represent cost-savings to researchers in terms of bait costs and researcher visits to traps, as well as a lower number of image sequences to process. However, this result could also be an artefact of the proximity of CTs within a cluster. Due to the pronounced effect of time since bait on the detection probability of all species considered, it is fair to make the conclusion that including bait improves detection probability. Although increasing the distance between CTs may reduce any effect of interacting scent lures, it is uncertain what distance could achieve this without extending beyond an area that represents a contiguous small mammal home range. For instance, GWTS has an estimated home range with a radius of 5\u0026ndash;7 metres (Talleraas, \u003cspan citationid=\"CR59\" class=\"CitationRef\"\u003e2021\u003c/span\u003e), and so we would be cautious about increasing the distance between traps. In general, the uncertainty in small mammal home ranges complicates study design. Even if we could optimise based on the home range sizes of the shrew species observed, the scale of home ranges is larger for mice or voles (Frafjord, \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e2024\u003c/span\u003e; Hummell et al., \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). Even within species, there could be dynamism in the home range size depending on whether surveys are carried out in the breeding season or not, and whether the individual is male or female (Frafjord, \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). Advances in small mammal tracking with GPS or radio collars continue to provide clearer inference regarding small mammal home ranges (Frafjord, \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e2024\u003c/span\u003e; Hummell et al., \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Makuya and Schradin, \u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e2023\u003c/span\u003e), but the remaining uncertainty is problematic, and trap proximity may have confounded the effect of different bait treatments.\u003c/p\u003e\u003cp\u003e\u003cb\u003eStatus of the GWTS in the UK\u003c/b\u003e\u003c/p\u003e\u003cp\u003eThe occupancy of GWTS was predicted to be 0.06 (0.03\u0026ndash;0.15) representing the lowest value for all observed species, excluding the water shrew. In contrast, pygmy shrews were recorded in over 1,000 sequences, with an occupancy of 0.55 (0.46\u0026ndash;0.65); we found a similar result for common shrews, with an occupancy of 0.39 (0.29\u0026ndash;0.51), 0.84 (0.52\u0026ndash;0.96) and 0.63 (0.42\u0026ndash;0.80) in woodland, grassland, and farmland habitats, respectively. For the native small mammals observed in this survey, relative frequencies from our CTs correlated well with NBN Atlas records for the region. This result, combined with a predicted occupancy an order of magnitude lower than native shrew species, should provide some reassurance that there is, as yet, no detectable negative impact of the non-native species, despite our survey being carried out in the centre of its currently known range. We recorded more sequences of GWTS than would be predicted in the region based on the rate of reporting via the NBN Atlas (Fig.\u0026nbsp;8), probably because our survey was carried out in the centre of its known distribution, and because the species is likely under-reported by the public. Although the potential of GWTS to rapidly displace pygmy shrews has been demonstrated in Ireland, this is not yet evident in our study area. In Ireland, the disappearance of pygmy shrews from sites where GWTS has become established can happen in as little as 1 year after contact (McDevitt et al., \u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e2014\u003c/span\u003e) whereas, in Sunderland where GWTS has likely been present for several years (Bond et al., \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2022\u003c/span\u003e), pygmy shrews remain present.\u003c/p\u003e\u003cp\u003eConcern regarding the presence of GWTS in Britain is justified given the species\u0026rsquo; potential to negatively impact native biodiversity, but the ecological contexts of Ireland and the UK are substantially different. As the sole species of shrew present in Ireland prior to the appearance of GWTS, Irish pygmy shrews\u0026rsquo; naivety to resource competition might account for the species\u0026rsquo; rapid displacement (Browett et al., \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). In the initial stages of the invasion in Ireland, the current explanation is that GWTS exploited larger invertebrate prey items that were not utilised by pygmy shrews. After depleting these resources, a switch to smaller prey items on which pygmy shrews depend created resource competition, from which the GWTS emerged as the stronger competitor (Browett et al., \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). In Britain, this process could be buffered by the prior existence of a multi-shrew community where niche separation has facilitated coexistence through resource partitioning. Rather than arriving to abundant large invertebrate food resources, GWTS would immediately have been in competition with the similarly sized common shrew, potentially reducing its rapid expansion in Britain (McDevitt, \u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e2023\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eIn its native range, GWTS coexists with pygmy shrews, with both species common on agricultural land on the island of Belle Ile, amongst a small mammal assemblage comparable to Ireland\u0026rsquo;s (McDevitt et al., \u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e2014\u003c/span\u003e). Consequently, it is hard to infer the specific causes of pygmy shrew exclusion in Ireland. Considering an ecological context more like that of the UK, further insight can be drawn from Norway\u0026rsquo;s experience of this species. GWTS was recorded for the first time in Norway in 2012, the northernmost record for this species in the world (Kooij and Nyfors, \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). Here, the species was first discovered via a public record. Subsequently, targeted media calls elicited more observations from the public, with some specimens being forwarded to researchers for further analysis. In reaction to the increasing records, a dedicated survey was carried out using camera traps in addition to pitfall traps and records from the public. GWTS was reported to occupy a proportion of the survey area comparable to that of several native small mammals (field vole, common shrew, pygmy shrew), but no negative impact was discernible (Talleraas, \u003cspan citationid=\"CR59\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). Further, GWTS and pygmy shrew were present at the same sites. While our results suggest that GWTS occupies a lower proportion of the survey area than native UK small mammal species, our finding of no detectable impact on native small mammals in the UK aligns with this result from Norway. Together, these results strengthen the hypothesis that different ecological contexts can explain differential impacts of non-native GWTS between Ireland, the UK, and Norway.\u003c/p\u003e\u003cp\u003eAside from impacts on small mammals, GWTS can also act as a reservoir host for pathogens that cause leptospirosis, a zoonotic disease. A novel \u003cem\u003eLeptospira\u003c/em\u003e pathogen was identified in the invasive Irish GWTS population, which might implicate disease-mediated interactions in the disappearance of the Irish pygmy shrew, although exact consequences are yet to be determined (Nally et al., \u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e2016\u003c/span\u003e). The presence of GWTS has also been associated with negative impacts on invertebrates: reduced species richness, reduced abundance, shorter arthropod body length and lower arthropod biomass have been reported in invaded relative to uninvaded zones in Ireland, sparking concern regarding disruption to farmland ecosystem services such as nutrient cycling, as well as potential impacts on farmland birds (Montgomery et al., \u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). From a social and economic viewpoint, GWTS\u0026rsquo;s tendency to take up residence in homes could also present pest control challenges (McDevitt, \u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e2023\u003c/span\u003e).\u003c/p\u003e"},{"header":"Conclusion","content":"\u003cp\u003eFuture research should prioritise further monitoring to better understand the spread of GWTS in the UK and continue to monitor for potential negative interactions with native species. The design for adapted CTs pioneered by Littlewood et al (\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e2021\u003c/span\u003e) and refined in this research offers a template for adapting commercially available CTs for the purpose of small mammal monitoring, and we recommend that if bait is applied, mealworms should be included to maximise the capture of shrews. Expanding the survey area beyond what was possible within this project could provide further information on any further range expansion of GWTS. Taking an example from Norway, targeted media calls for citizens to submit any shrew observations to a citizen science platform effectively surveyed a much larger area than possible by researchers alone (Kooij and Nyfors, \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e2023\u003c/span\u003e) and a similar scheme in the UK could provide evidence of populations not currently known to researchers. Planned genetic analysis of the UK GWTS population will be insightful in determining the likely source of the non-native population (McDevitt, \u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e2023\u003c/span\u003e) and, hopefully, can inform management solutions to prevent such transport in the future. Our survey results, and those of Talleraas (\u003cspan citationid=\"CR59\" class=\"CitationRef\"\u003e2021\u003c/span\u003e) in Norway, provide some reassurance that GWTS\u0026rsquo;s impacts might not always be as severe as reported in Ireland. Nevertheless, we would recommend caution in extrapolating the results of this small-scale survey, given the known capabilities of GWTS as a formidable competitor in Ireland. There remain several ways in which this species could cause damage, beyond its potential impacts on small mammal communities.\u003c/p\u003e"},{"header":"Statements and Declarations","content":"\u003cp\u003eAcknowledgements\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eWe gratefully acknowledge the contributions of Vivien Kent, Terry Coult, and Ian Bond, who kindly shared small mammal live-trapping survey results with us that guided the deployments of camera traps in this work. Further thanks go to Tom Stephenson and David McGregor at Sunderland City Council, as well as Jim Cokill at the Durham Wildlife Trust, and Joe Davies at Castle Eden Dene National Nature Reserve for facilitating access to survey sites during the summer of 2024. Our thanks also go to Dr Christine Howard providing a record of the non-native shrew \u003cem\u003eCrocidura russula\u003c/em\u003e out-with its then known range, aiding our survey site selection.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding:\u0026nbsp;\u003c/strong\u003eFinancial support for the purchase of field equipment was received from the Animal and Plant Health agency (APHA).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests:\u003c/strong\u003e The authors have no competing interests to declare that are relevant to the content of this article.\u0026nbsp;\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eAvotins A, Ķerus V, Aunins A (2023) Numerical Response of Owls to the Dampening of Small Mammal Population Cycles in Latvia. Life 13:572. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.3390/life13020572\u003c/span\u003e\u003cspan address=\"10.3390/life13020572\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eBartoń K (2024) MuMIn: Multi-model inference (manual)\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eBeery S, Morris D, Yang S (2019) Efficient Pipeline for Camera. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.48550/arXiv.1907.06772\u003c/span\u003e\u003cspan address=\"10.48550/arXiv.1907.06772\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e. Trap Image Review\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eBergeson SM, Kays R, Jachowski DS, Anderson CD, Williamson CR, Burket A, Butfiloski JW, Cheeseman AE, Cotey SR, Dennison CC, Erb JD, Farris ZJ, Fies ML, Joyce MJ, Olfenbuttel C, Sasse B, Smith LM, Tabora JA, Zimova M (2025) Efficacy of baits and lures for weasel detection. Wildl Soc Bull 49:e1580. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1002/wsb.1580\u003c/span\u003e\u003cspan address=\"10.1002/wsb.1580\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eBond I, Gilford E, McDevitt A, Young M, Coomber F (2022) First records of the greater white-toothed shrew \u003cem\u003eCrocidura russula\u003c/em\u003e from Great Britain. Mammal Com 8. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.59922/AACI6532\u003c/span\u003e\u003cspan address=\"10.59922/AACI6532\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eBrowett SS, Synnott R, O\u0026rsquo;Meara DB, Antwis RE, Browett SS, Bown KJ, Wangensteen OS, Dawson DA, Searle JB, Yearsley JM, McDevitt AD (2023) Resource competition drives an invasion-replacement event among shrew species on an island. J Anim Ecol 92:698\u0026ndash;709. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1111/1365-2656.13855\u003c/span\u003e\u003cspan address=\"10.1111/1365-2656.13855\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eBurns et al (2023) State of Nature 2023 - report on the UK\u0026rsquo;s current biodiversity [WWW Document]. State of Nature. URL \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://stateofnature.org.uk/\u003c/span\u003e\u003cspan address=\"https://stateofnature.org.uk/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (accessed 02/08/24)\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eCasta\u0026ntilde;eda I, Pisanu B, D\u0026iacute;az M, R\u0026eacute;zouki C, Baudry E, Chapuis J-L, Bonnaud E (2018) Minimising trapping effort without affecting population density estimations for small mammals. Mamm Biol 93:144\u0026ndash;152. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/j.mambio.2018.10.004\u003c/span\u003e\u003cspan address=\"10.1016/j.mambio.2018.10.004\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eCoomber FG, Smith BR, August TA, Harrower CA, Powney GD, Mathews F (2021) Using biological records to infer long-term occupancy trends of mammals in the UK. Biol Conserv 264:109362. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/j.biocon.2021.109362\u003c/span\u003e\u003cspan address=\"10.1016/j.biocon.2021.109362\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eCroft S, Chauvenet ALM, Smith GC (2017) A systematic approach to estimate the distribution and total abundance of British mammals. PLoS ONE 12:e0176339. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1371/journal.pone.0176339\u003c/span\u003e\u003cspan address=\"10.1371/journal.pone.0176339\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eDe Bondi N, White J, Stevens M, Cooke R (2010) A comparison of the effectiveness of camera trapping and live trapping for sampling terrestrial small-mammal communities. Wildl Res 37:456\u0026ndash;465. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1071/WR10046\u003c/span\u003e\u003cspan address=\"10.1071/WR10046\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eDEFRA (2021) World-leading Environment Act becomes law [WWW Document]. GOV.UK. URL \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.gov.uk/government/news/world-leading-environment-act-becomes-law\u003c/span\u003e\u003cspan address=\"https://www.gov.uk/government/news/world-leading-environment-act-becomes-law\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (accessed 02/11/25)\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eDo R, Shonfield J, McAdam AG (2013) Reducing accidental shrew mortality associated with small-mammal livetrapping II: a field experiment with bait supplementation. J Mammal 94:754\u0026ndash;760. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1644/12-MAMM-A-242.1\u003c/span\u003e\u003cspan address=\"10.1644/12-MAMM-A-242.1\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eDueser RD, Porter JH, Moncrief ND (2025) The continuing search for a better mouse trap: Two tests of a practical, low-cost camera trap for detecting and observing small mammals. PLoS ONE 20:e0309252. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1371/journal.pone.0309252\u003c/span\u003e\u003cspan address=\"10.1371/journal.pone.0309252\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eDuffie DR, Gitzen RA, Sharp NW, Turner AJ (2019) Effectiveness and Accuracy of Track Tubes for Detecting Small-Mammal Species Occupancy in Southeastern Herbaceous Wetlands and Meadows. Southeast Nat 18:130\u0026ndash;146. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1656/058.018.0109\u003c/span\u003e\u003cspan address=\"10.1656/058.018.0109\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eFerreira C, Delibes-Mateos M (2012) Conflictive management of small mammals considered as pests: A long way to evidence-based policy making. Curr Zool 58:353\u0026ndash;357. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1093/czoolo/58.2.353\u003c/span\u003e\u003cspan address=\"10.1093/czoolo/58.2.353\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eFink M, Jachowski D (2025) Comparison of three camera trap designs for sampling small mammals. Mamm Res 1\u0026ndash;8. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1007/s13364-025-00780-7\u003c/span\u003e\u003cspan address=\"10.1007/s13364-025-00780-7\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eFiske I, Chandler R (2011) unmarked: An R Package for Fitting Hierarchical Models of Wildlife Occurrence and Abundance. J Stat Softw 43:1\u0026ndash;23. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.18637/jss.v043.i10\u003c/span\u003e\u003cspan address=\"10.18637/jss.v043.i10\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eFrafjord K (2024) Dynamic home range of the grey-sided vole \u003cem\u003eCraseomys rufocanus\u003c/em\u003e: a pilot study. BMC Zool 9:16. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1186/s40850-024-00209-9\u003c/span\u003e\u003cspan address=\"10.1186/s40850-024-00209-9\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eGod\u0026oacute; L, Valk\u0026oacute; O, Borza S, De\u0026aacute;k B (2022) A global review on the role of small rodents and lagomorphs (clade Glires) in seed dispersal and plant establishment. Glob Ecol Conserv 33:e01982. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/j.gecco.2021.e01982\u003c/span\u003e\u003cspan address=\"10.1016/j.gecco.2021.e01982\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eGracanin A, Gracanin V, Mikac KM (2019) The selfie trap: A novel camera trap design for accurate small mammal identification. Ecol Manag Restor 20:156\u0026ndash;158. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1111/emr.12345\u003c/span\u003e\u003cspan address=\"10.1111/emr.12345\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eGracanin A, Minchinton TE, Mikac KM (2022) Estimating the density of small mammals using the selfie trap is an effective camera trapping method. Mammal Res 67:467\u0026ndash;482. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1007/s13364-022-00643-5\u003c/span\u003e\u003cspan address=\"10.1007/s13364-022-00643-5\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eHeisler LM, Somers CM, Poulin RG (2016) Owl pellets: a more effective alternative to conventional trapping for broad-scale studies of small mammal communities. Methods Ecol Evol 7:96\u0026ndash;103. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1111/2041-210X.12454\u003c/span\u003e\u003cspan address=\"10.1111/2041-210X.12454\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eHsing PY, Hill RA, Smith GC, Bradley S, Green SE, Kent VT, Mason SS, Rees J, Whittingham MJ, Cokill J, Stephens PA, MammalWeb Citizen S (2022) Large-scale mammal monitoring: The potential of a citizen science camera-trapping project in the United Kingdom. Ecol Solut Evid 3. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1002/2688-8319.12180\u003c/span\u003e\u003cspan address=\"10.1002/2688-8319.12180\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eHummell GF, Li AY, Mullinax JM (2022) Very small collars: an evaluation of telemetry location estimators for small mammals. Anim Biotelemetry 10:1\u0026ndash;14. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1186/s40317-022-00301-2\u003c/span\u003e\u003cspan address=\"10.1186/s40317-022-00301-2\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eHurst ZM, McCleery RA, Collier BA, Silvy NJ, Taylor PJ, Monadjem A (2014) Linking changes in small mammal communities to ecosystem functions in an agricultural landscape. Mamm Biol 79:17\u0026ndash;23. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/j.mambio.2013.08.008\u003c/span\u003e\u003cspan address=\"10.1016/j.mambio.2013.08.008\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eKalinin AA, Aleksandrov DY (2023) Residents and Nonresidents: Daily Activity of Small Mammals in Contrasting Biotopes. Russ J Ecol 54:392\u0026ndash;401. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1134/S1067413623050053\u003c/span\u003e\u003cspan address=\"10.1134/S1067413623050053\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eKellner KF, Smith AD, Royle JA, K\u0026eacute;ry M, Belant JL, Chandler RB (2023) The unmarked R package: Twelve years of advances in occurrence and abundance modelling in ecology. Methods Ecol Evol 14:1408\u0026ndash;1415. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1111/2041-210X.14123\u003c/span\u003e\u003cspan address=\"10.1111/2041-210X.14123\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eKok A, Parker D, Barker N (2013) Rules of attraction: the role of bait in small mammal sampling at high altitude in South Africa. Afr Zool 48:84\u0026ndash;95. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1080/15627020.2013.11407571\u003c/span\u003e\u003cspan address=\"10.1080/15627020.2013.11407571\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003evan der Kooij J, Nyfors E (2023) Citizen science reveals the first occurrence of the greater white-toothed shrew \u003cem\u003eCrocidura russula\u003c/em\u003e in Fennoscandia. Mammalia 87:442\u0026ndash;450. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1515/mammalia-2023-0042\u003c/span\u003e\u003cspan address=\"10.1515/mammalia-2023-0042\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eKorpim\u0026auml;ki E, Norrdahl K, Huitu O, Klemola T (2005) Predator-induced synchrony in population oscillations of coexisting small mammal species. Proc. R. Soc. B-Biol. Sci. 272, 193\u0026ndash;202. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1098/rspb.2004.2860\u003c/span\u003e\u003cspan address=\"10.1098/rspb.2004.2860\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eLinds\u0026oslash; LK, Rivrud IM, K\u0026ouml;nig EA, Herland A, Mysterud A (2025) Diel Niche of Sympatric Small Mammals Revealed by Year-Round Camera Trapping. Ecol Evol 15:e71590. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1002/ece3.71590\u003c/span\u003e\u003cspan address=\"10.1002/ece3.71590\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eLittlewood NA, Hancock MH, Newey S, Shackelford G, Toney R (2021) Use of a novel camera trapping approach to measure small mammal responses to peatland restoration. Eur J Wildl Res 67. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1007/s10344-020-01449-z\u003c/span\u003e\u003cspan address=\"10.1007/s10344-020-01449-z\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eLong RA, MacKay P, Sauder JD, Sinclair M, Aubry KB, Raley CM (2024) An overwinter protocol for detecting wolverines and other carnivores at camera traps paired with automated scent dispensers. Ecol Evol 14:e11290. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1002/ece3.11290\u003c/span\u003e\u003cspan address=\"10.1002/ece3.11290\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eMacKenzie DI, Bailey LL (2004) Assessing the fit of site-occupancy models. J Agri Biol Env Stat 9:300\u0026ndash;318. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1198/108571104X3361\u003c/span\u003e\u003cspan address=\"10.1198/108571104X3361\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eMacKenzie DI, Nichols JD, Lachman GB, Droege S, Andrew Royle J, Langtimm CA (2002) Estimating Site Occupancy Rates When Detection Probabilities Are Less Than One. Ecol 83:2248\u0026ndash;2255. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1890/0012-9658\u003c/span\u003e\u003cspan address=\"10.1890/0012-9658\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e(2002)083[2248:ESORWD]2.0.CO;2\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eMakuya L, Schradin C (2023) Measuring range sizes in a 100-g rodent: mini-GPS are more reliable than transmitters, but the location error reduces reliability. Mamm Biol 103:455\u0026ndash;465. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1007/s42991-023-00365-4\u003c/span\u003e\u003cspan address=\"10.1007/s42991-023-00365-4\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eMazerolle MJ (2023) AICcmodavg: Model selection and multimodel inference based on (Q)AIC(c) (manual)\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eMcCleery RA, Zweig CL, Desa MA, Hunt R, Kitchens WM, Percival HF (2014) A Novel Method for Camera-Trapping Small Mammals. Wildl Soc Bull 38:887\u0026ndash;891. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1002/wsb.447\u003c/span\u003e\u003cspan address=\"10.1002/wsb.447\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eMcDevitt A (2023) Rapid Risk Assessment Summary Sheet: \u003cem\u003eCrocidura russula\u003c/em\u003e (greater white-toothed shrew) [WWW Document]. URL \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.nonnativespecies.org/assets/Uploads/Crocidura-russula-RRA-final.pdf\u003c/span\u003e\u003cspan address=\"https://www.nonnativespecies.org/assets/Uploads/Crocidura-russula-RRA-final.pdf\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (accessed 11/21/24)\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eMcDevitt AD, Montgomery WI, Tosh DG, Lusby J, Reid N, White TA, McDevitt CD, O\u0026rsquo;Halloran J, Searle JB, Yearsley JM (2014) Invading and Expanding: Range Dynamics and Ecological Consequences of the Greater White-Toothed Shrew (\u003cem\u003eCrocidura russula\u003c/em\u003e) Invasion in Ireland. PLoS ONE 9:e100403. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1371/journal.pone.0100403\u003c/span\u003e\u003cspan address=\"10.1371/journal.pone.0100403\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eMcShea WJ, Forrester T, Costello R, He Z, Kays R (2016) Volunteer-run cameras as distributed sensors for macrosystem mammal research. Landsc Ecol 31:55\u0026ndash;66. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1007/s10980-015-0262-9\u003c/span\u003e\u003cspan address=\"10.1007/s10980-015-0262-9\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eMontgomery WI, Montgomery SSJ, O\u0026rsquo;Neill J, Reid N (2023) Invasion of farmland hedgerows by non-native small mammals is associated with lower soil surface invertebrate diversity, abundance, body size and biomass. Biol Invasions. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1007/s10530-023-03199-w\u003c/span\u003e\u003cspan address=\"10.1007/s10530-023-03199-w\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eMos J, Hofmeester TR (2020) The \u003cem\u003eMostela\u003c/em\u003e: an adjusted camera trapping device as a promising non-invasive tool to study and monitor small mustelids. Mamm Res 65:843\u0026ndash;853. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1007/s13364-020-00513-y\u003c/span\u003e\u003cspan address=\"10.1007/s13364-020-00513-y\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eNally JE, Arent Z, Bayles DO, Hornsby RL, Gilmore C, Regan S, McDevitt AD, Yearsley J, Fanning S, McMahon BJ (2016) Emerging Infectious Disease Implications of Invasive Mammalian Species: The Greater White-Toothed Shrew (\u003cem\u003eCrocidura russula\u003c/em\u003e) Is Associated With a Novel Serovar of Pathogenic Leptospira in Ireland. Plos Negl Trop Dis 10:e0005174. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1371/journal.pntd.0005174\u003c/span\u003e\u003cspan address=\"10.1371/journal.pntd.0005174\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eNBN Trust (2025) The National Biodiversity Network (NBN) Atlas. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://ror.org/00mcxye41\u003c/span\u003e\u003cspan address=\"https://ror.org/00mcxye41\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003ePaull DJ, Claridge AW, Barry SC (2011) There\u0026rsquo;s no accounting for taste: bait attractants and infrared digital cameras for detecting small to medium ground-dwelling mammals. Wildl Res 38:188\u0026ndash;195. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1071/WR10203\u003c/span\u003e\u003cspan address=\"10.1071/WR10203\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003ePoe N, Stuble KL, Souza L (2019) Small mammal herbivores mediate the effects of soil nitrogen and invertebrate herbivores on grassland diversity. Ecol Evol 9:3577\u0026ndash;3587. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1002/ece3.4991\u003c/span\u003e\u003cspan address=\"10.1002/ece3.4991\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eProbst R, Probst R (2023) High Frequency of \u003cem\u003eApodemus\u003c/em\u003e Mice Boosts Inverse Activity Pattern of Bank Voles, \u003cem\u003eClethrionomys glareolus\u003c/em\u003e, through Non-Aggressive Intraguild Competition. Anim 13:981. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.3390/ani13060981\u003c/span\u003e\u003cspan address=\"10.3390/ani13060981\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eRendall AR, Sutherland DR, Cooke R, White J (2014) Camera Trapping: A Contemporary Approach to Monitoring Invasive Rodents in High Conservation Priority Ecosystems. PLoS ONE 9:e86592. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1371/journal.pone.0086592\u003c/span\u003e\u003cspan address=\"10.1371/journal.pone.0086592\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eRichards SA, Whittingham MJ, Stephens PA (2011) Model selection and model averaging in behavioural ecology: the utility of the IT-AIC framework. Behav Ecol Sociobiol 65:77\u0026ndash;89. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1007/s00265-010-1035-8\u003c/span\u003e\u003cspan address=\"10.1007/s00265-010-1035-8\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eRimbach R, Heinze K, Poorthuis L, Petit J, Dammhahn M (2025) City life does not change a small mammal community composition. Wildl Res 52. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1071/WR24196\u003c/span\u003e\u003cspan address=\"10.1071/WR24196\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eShonfield J, Do R, Brooks RJ, McAdam AG (2013) Reducing accidental shrew mortality associated with small-mammal livetrapping I: an inter- and intrastudy analysis. J Mammal 94:745\u0026ndash;753. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1644/12-MAMM-A-271.1\u003c/span\u003e\u003cspan address=\"10.1644/12-MAMM-A-271.1\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eSmaal M, van Manen W (2022) Detecting and monitoring small mammals with trail cameras. Lutra 65:247\u0026ndash;257\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eSmith G, Bond IF, Coult T, Henderson D, Graham C, Brand E, McDevitt AD, Stephens PA, Webb S, Larsen-Daw M (in review) \u0026lsquo;Investigating the greater white-toothed shrew invasion in Great Britain\u0026rsquo; \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.21203/rs.3.rs-5849552/v1\u003c/span\u003e\u003cspan address=\"10.21203/rs.3.rs-5849552/v1\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eSoininen EM, Jensvoll I, Killengreen ST, Ims RA (2015) Under the snow: a new camera trap opens the white box of subnivean ecology. Remote Sens Ecol Conserv 1:29\u0026ndash;38. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1002/rse2.2\u003c/span\u003e\u003cspan address=\"10.1002/rse2.2\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eStromgren EJ, Sullivan TP (2014) Influence of pitfall versus Longworth livetraps, bait addition, and drift fences on trap success and mortality of shrews. Acta Theriol 59:203\u0026ndash;210. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1007/s13364-013-0149-6\u003c/span\u003e\u003cspan address=\"10.1007/s13364-013-0149-6\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eSwanson A, Kosmala M, Lintott C, Simpson R, Smith A, Packer C (2015) Snapshot Serengeti, high-frequency annotated camera trap images of 40 mammalian species in an African savanna. Sci Data 2:150026. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1038/sdata.2015.26\u003c/span\u003e\u003cspan address=\"10.1038/sdata.2015.26\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eTalleraas SM (2021) Estimating and analysing the occupancy of the introduced shrew \u003cem\u003eCrocidura russula\u003c/em\u003e in Norway (Master thesis). Norwegian University of Life Sciences, \u0026Aring;s\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eTosh DG, Lusby J, Montgomery WI, O\u0026rsquo;halloran J (2008) First record of greater white-toothed shrew \u003cem\u003eCrocidura russula\u003c/em\u003e in Ireland. Mamm Rev 38:321\u0026ndash;326. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1111/j.1365-2907.2008.00130.x\u003c/span\u003e\u003cspan address=\"10.1111/j.1365-2907.2008.00130.x\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eVerhees JJF, van der Putten TAW, van Hoof PH, Heijkers D, Lemmers P, Esser HJ, de Boer WF (2024) Comparing the effectiveness of short-focal camera trapping, live trapping, and soil eDNA for surveying small mammals: A case study on Eurasian water shrew (\u003cem\u003eNeomys fodiens\u003c/em\u003e). Eur J Wildl Res 70:13. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1007/s10344-023-01760-5\u003c/span\u003e\u003cspan address=\"10.1007/s10344-023-01760-5\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eVillette P, Krebs CJ, Jung TS, Boonstra R (2016) Can camera trapping provide accurate estimates of small mammal (\u003cem\u003eMyodes rutilus\u003c/em\u003e and \u003cem\u003ePeromyscus maniculatus\u003c/em\u003e) density in the boreal forest? J Mammal 97:32\u0026ndash;40. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1093/jmammal/gyv150\u003c/span\u003e\u003cspan address=\"10.1093/jmammal/gyv150\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eVogel P, Jutzeler S, Rulence B, Reutter BA (2002) Range expansion of the greater white-toothed shrew \u003cem\u003eCrocidura russula\u003c/em\u003e in Switzerland results in local extinction of the bicoloured white-toothed shrew \u003cem\u003eC. leucodon\u003c/em\u003e. Acta Theriol 47:15\u0026ndash;24. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1007/BF03193562\u003c/span\u003e\u003cspan address=\"10.1007/BF03193562\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"european-journal-of-wildlife-research","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"ejwr","sideBox":"Learn more about [European Journal of Wildlife Research](http://link.springer.com/journal/10344)","snPcode":"10344","submissionUrl":"https://submission.nature.com/new-submission/10344/3","title":"European Journal of Wildlife Research","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false},"keywords":"Camera trap, small mammal monitoring, shrew","lastPublishedDoi":"10.21203/rs.3.rs-7083063/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7083063/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eIn recent decades, motion sensor camera traps have revolutionised wildlife monitoring as a cost-effective strategy requiring less time investment than traditional monitoring methods. While medium-to-large body sized mammals are captured at sufficient resolution to permit confident species identifications, small mammal species (mice, voles, and shrews) are difficult to distinguish in conventional camera trap imagery. Since camera traps represent a potential solution for overcoming spatial and temporal constraints of traditional small mammal survey methodologies (live trapping), novel designs have materialised in recent years to adjust camera traps for observing smaller animals. In this research, we further refined an existing design, the Littlewood box, and investigated the optimal bait strategy to maximise small mammal detections in the Northeast of England within the currently known range of the non-native greater white toothed shrew, \u003cem\u003eCrocidura russula\u003c/em\u003e. We found no significant difference in the probability of detection of small mammal species by bait type, but there were greater numbers of captures of shrew species at traps baited with mealworms. We conclude that the use of bait is associated with a greater number of captures for all small mammal species observed compared to non-baited traps. Despite the cameras being deployed in the centre of the known range of \u003cem\u003eC. russula\u003c/em\u003e in Britain, this species was present at a lower proportion of sites than native small mammals.\u003c/p\u003e","manuscriptTitle":"Camera trapping for small mammals: the case of a non- native shrew","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-10-29 15:47:41","doi":"10.21203/rs.3.rs-7083063/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2025-12-31T07:49:29+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-11-02T14:33:39+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"140521166798509056480995982733856677100","date":"2025-10-21T06:26:03+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"108482705978800875125932931005826850490","date":"2025-10-20T08:00:33+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"131129287283538049107590747278581858070","date":"2025-10-18T18:51:16+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"205933921424450029181123906481599686091","date":"2025-10-17T15:57:04+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2025-10-15T14:34:26+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-07-15T12:41:17+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2025-07-15T12:40:47+00:00","index":"","fulltext":""},{"type":"submitted","content":"European Journal of Wildlife Research","date":"2025-07-09T10:49:34+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"european-journal-of-wildlife-research","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"ejwr","sideBox":"Learn more about [European Journal of Wildlife Research](http://link.springer.com/journal/10344)","snPcode":"10344","submissionUrl":"https://submission.nature.com/new-submission/10344/3","title":"European Journal of Wildlife Research","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false}}],"origin":"","ownerIdentity":"a4d1e40f-62bf-46df-97e3-44330923224f","owner":[],"postedDate":"October 29th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[],"tags":[],"updatedAt":"2026-02-02T16:04:51+00:00","versionOfRecord":{"articleIdentity":"rs-7083063","link":"https://doi.org/10.1007/s10344-026-02052-4","journal":{"identity":"european-journal-of-wildlife-research","isVorOnly":false,"title":"European Journal of Wildlife Research"},"publishedOn":"2026-01-30 15:59:08","publishedOnDateReadable":"January 30th, 2026"},"versionCreatedAt":"2025-10-29 15:47:41","video":"","vorDoi":"10.1007/s10344-026-02052-4","vorDoiUrl":"https://doi.org/10.1007/s10344-026-02052-4","workflowStages":[]},"version":"v1","identity":"rs-7083063","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7083063","identity":"rs-7083063","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}