IntroductionThe natural world is experiencing immense change, driven by increasing human populations, climate change, and the degradation in quality of key resources such as natural habitats and water (Maja and Ayano, 2021). These rapid and widespread environmental changes have created conditions that far exceed the adaptive capacities of many species, leading to significant declines in wildlife populations and the degradation of biodiversity globally (Isbell et al. 2023). This loss of biodiversity not only threatens the survival of individual species but also undermines the integrity of ecosystems that provide essential services to both people and nature (Isbell et al. 2023). Systems that monitor and manage wildlife in areas of land-use change are crucial for disentangling the complex effects that change has on wildlife populations and their habitats (McRae et al. 2008; Murphy et al. 2023a). Without these systems in place, we will fail to understand how rapid change impacts key ecological and economic pillars, such as agricultural productivity, zoonotic disease emergence, and the resilience of natural resources (McMahon et al. 2018). In many countries, preventative systems are in place to identify likely impacts of land-use change and mitigate against them, in the form of policy instruments such as Environmental Impact Assessment (Glasson, and Therivel, 2013). Unfortunately, these processes typically fail to identify multivariate consequences of land-use change that occur over time and space (Murphy et al. 2022).Wildlife responses to human-mediated land-use changes, such as construction, forestry, and agriculture, can occur over varying temporal and spatial scales (Allen et al. 2019; Murphy et al. 2022). These changes may drive species into smaller refugia near human populations, increasing densities and exacerbating conflicts, often mistakenly attributed to wildlife abundance rather than the initial land-use change (Pozo et al. 2017; Fowler et al. 2019; Murphy et al. 2023a). When planned, land-use changes like road construction or forest removal offer opportunities to predict wildlife responses and validate hypotheses with empirical data, informing proactive mitigation strategies (Gaughran et al. 2021; Murphy et al. 2022). One important ecological process impacted by landscape disturbance is disease dynamics (Brearley et al. 2013). Landscape changes, such as habitat fragmentation and alteration, can influence wildlife spatial ecology, leading to increased interactions between wildlife, domestic animals, and humans, thereby facilitating the spread of diseases (Daszak et al. 2001). One notable example is bovine tuberculosis (bTB), a zoonotic disease with a global distribution and complex epidemiology due to the presence of multiple wildlife hosts, for instance, deer (Cervus & Odocoileus spp.) in North America (O’Brien et al. 2011), brush-tailed possums (Trichosurus vulpecula) in New Zealand (Tweddle and Livingstone 1994), buffalo (Syncerus caffer ) in South Africa (Davey 2023), and European badgers (Meles meles ) in Ireland and the UK, where they are the primary wildlife hosts implicated in the maintenance and transmission of bTB to local cattle herds (Allen et al. 2018; Chang et al. 2024). Recent studies have shown disturbances adjacent to agricultural landscapes have been linked to increased risk of bTB breakdowns in cattle herds (Barroso et al. 2022).However, our understanding of the mechanisms behind fluctuations in bTB risk remains incomplete due to conflicting evidence from different studies. It is hypothesized that disturbances alter disease risk by modifying wildlife behaviour and social structures, potentially forcing species like badgers to change their movement patterns and increasing inter/intra species interactions (Murphy et al. 2022). For instance, Barroso et al. (2022) observed an increased risk of bTB in cattle herds near roadworks, suggesting that such disturbances may contribute to disease spread. Conversely, Gaughran et al. (2021) found no significant changes in wildlife territoriality due to road construction and suggest that fully mitigated major road upgrades are unlikely to cause a perturbation effect substantial enough to increase TB in local cattle. This discrepancy underscores the complexity of disease dynamics in disturbed landscapes and emphasises the need for more integrated research that combines ecological, epidemiological, and human factors. The example of how bTB continues to proliferate despite surveillance and control of the disease highlights the necessity for deeper investigation into how macroecological human-mediated landscape disturbances may impact disease and the need to develop effective strategies for mitigating disease risk and spread.Previous research in Ireland has explored the link between landscape disturbances, such as forest clearfells, and bTB risk in cattle herds. Byrne et al. (2022) found that the percentage of herds testing positive for bTB increased after clearfelling, potentially due to disturbance of badger populations, a key wildlife host (Mullen et al. 2019). While unable to infer the mechanism, their study highlighted the need to integrate epidemiology with wildlife ecology. Murphy et al. (2022) extended this work by incorporating ecological covariates, showing that the relationship between clearfells and bTB risk was dynamic, depending on the extent and timing of clearfell activities relative to cattle farms and surrounding habitats. Murphy et al. (2022) hypothesised that badgers may initially leave disturbed areas but return as vegetation regenerates, altering disease transmission in time and space.Finally, Khouri et al. (2023) examined the role of wildlife densities, including badgers and deer, in influencing bTB risk after clearfell. Their study found that active badger sett density consistently predicted increased bTB risk, particularly when clearfells occurred within 2 km of farms, 24-36 months prior. Khouri et al. (2023) call for simulation approaches using agent-based models (ABMs) to simulate individual wildlife behaviours in response to habitat disturbances like clearfells. ABMs can reflect adaptive behaviours and environmental dynamics, offering insights into wildlife dispersal and interactions (Murphy et al. 2020). Additionally, GPS tracking data for wildlife hosts could provide valuable insights on how habitat disturbances affect wildlife movement and contacts with cattle, thereby informing more effective bTB management strategies (Conteddu et al. 2024).In this study, we developed a highly realistic agent-based model (ABM) to simulate wildlife responses to clearfell forestry at 100 sites across Ireland, focusing on the potential link between ecological disturbance and bTB risk. Clearfell forestry, where all marketable trees are harvested at the end of a rotation (typically 30–50 years for conifers), potentially displaces wildlife, thus altering their movement patterns and contact rates with cattle. We hypothesised that such disturbances modulate wildlife movement, increasing the risk of new encounters between wildlife hosts and cattle. Our ABM was parametrised using historical data from 202 individually tracked badgers across two study regions in Ireland, which had experienced both Test and Vaccinate or Remove (TVR) operations and a large road improvement scheme. We also integrated high-resolution GIS data, including spatial information on farms, clearfell sites, habitats, forestry, and badger setts.Methods To simulate the behavioural response of European badgers to clearfell forestry in Ireland and assess the relative impact on bovine tuberculosis, we gathered a mixture of tracking data and field survey data (e.g. badger sett locations and activity) for badgers, and remote sensing spatial data for habitats, farms and forestry operations. We used these data to build a realistic agent-based model. The data, subsequent analysis and an overview of the agent-based model is described below however a comprehensive description of the agent-based model is described using the ODD protocol proposed by Grimm et al (2010) in Supplementary Material 1. All data management and statistical analysis was conducted in R version 4.4.1 (R Core Team, 2023) and all simulation modelling was done in Netlogo version 6.4.0 (Wilensky, 1999)Wildlife DataWe used data from GPS-collared badgers in two regions in Ireland and Northern Ireland, respectively. We used data from a badger tracking study conducted in Co. Wicklow conducted by Trinity College Dublin and from another badger tracking study in Co. Down conducted by the Veterinary Epidemiology Unit in the Department of Agriculture, Environment and Rural Affairs (DAERA). The Tellus Light GPS collar (Followit Wildlife, Lindsberg, Sweden) was used to capture data at one-hour fixes with an even spread of data accumulated across both regions (53% Co. Wicklow; 47% Co. Down). Data from both studies were compiled into a single database to be examined and screened for errors (i.e we removed incorrect fixes and incomplete tracks). Further details about each specific tracking study can be found at MacWhite et al. 2013 and Gaughran et al. 2021 for the Co. Wicklow study and O’Hagan et al. 2021 for the Co. Down study, respectively.Our final database consisted of 202 unique badgers tracked for the period 2010-2017 (Co. Wicklow study period 2010-2016; Co. Down study period 2014-2017), covering all times of the year (68% Apr-Oct; 32% Nov-Mar), age classes (88% Adults; 12% Juveniles), sexes (45% Females; 55% Males) and across a representative heterogenous landscape consisting of mixed farmland (grassland, arable, farm yards), mixed forestry, urban areas, among other habitats. Full details of the data analysis of the badger telemetry database are described below.We obtained badger sett survey data from the Department of Agriculture, Food and the Marine (DAFM) Wildlife Unit. The database contained 55,672 locations of surveyed setts across Ireland from the period 1996-2023. These data were filtered to exclude any sett type that was not a main sett (e.g. annex, secondary, outlier) so that only active main setts were included in the final database (see Thornton, 1988). Sett data are collected relative to cattle herd breakdowns, and therefore there are known areas without survey effort (Byrne et al. 2014). Therefore, the raw sett data was compared against the predictions from a sett relative abundance model to generate an accurate sett density for each site as laid out in Supplementary Material 1.Habitats Data We used CORINE 2018 land-use polygons data for both Ireland and Northern Ireland (UK) to classify habitat types using a consistent methodology across the study area. This dataset, available from the European Union’s Copernicus Land Monitoring Service (DOI: 10.2909/71c95a07-e296-44fc-b22b-415f42acfdf0) provides detailed classifications of land cover types at a 100-meter spatial resolution. We downloaded each national dataset and then merged the data from both Ireland and Northern Ireland into an integrated all-island database.Farm Data Spatial and administrative data for all farms in Ireland were also provided by DAFM via the Land Parcel Information System (LPIS). LPIS serves as a comprehensive database, consisting of over 135,000 farms, that registers and maps agricultural land parcels across the country, encompassing information on land use, boundaries, ownership and information on herd numbers & enterprise type. These data were provided by DAFM and are subject to stringent general data protection regulation (GDPR) requirements, whereby the minimum amount of data required for the purpose of the study is used and all personal data (e.g. herd numbers) are anonymised in the analysis.Forestry Data We collected all relevant spatial and administrative forestry data from Coillte, Ireland’s semi-state forest custodian. Coillte manage 57% (440,000 ha) of Ireland’s forestry, which is predominantly conifer high forest in clearfell rotations. Clearfell forestry is a rotational forestry system where all marketable trees are harvested within a forest at the end of a set rotation. Rotations generally between take place when trees are at an ages of 30 and 50 years in conifer forests, once trees are felled, they are replanted and the site typically regenerates over a period of 12-36 months. The species mixtures in our clearfell sites are typically comprised on non-native conifer species such as Sitka spruce Picea sitchensis , Japanese larch Larix kaempferi , lodgepole pine Pinus contorta , and Douglas fir Pseudotsuga menziesii .  We build a forestry database that included information on private forestry, public forestry, clearfell sites and forest boundaries. This database included the date of each forest clearfell event, the administrative boundary of the forest and the ID of each forest subplot felled within the property for the period 2015-2017.  Bovine Tuberculosis Testing Data The relevant bTB testing and breakdown history data for all farms in Ireland was obtained from the Department of Agriculture, Food and the Marine (DAFM) in collaboration with the UCD Centre for Veterinary Epidemiological Risk Analysis (CVERA). Every cattle herd in Ireland has at least one herd test per year. All cattle present on the farm on the day of the test are tested, with the exception of calves aged under 6 weeks, which were born on the farm. The test used is the single intradermal comparative tuberculin test (SICTT). The SICTT test is the main diagnostic test used in Ireland, though it is imperfect. The sensitivity and specificity of the test is known to vary with estimates of 52.9%–60.8% and 99.2%–99.8% respectively from field trials in Ireland (Clegg et al., 2011). The animals showing a positive reaction to the test are known as reactors, and farms with animals that test positive are known as breakdown herds.Wildlife GPS data analysis We used the amt package in R (Signer et al. 2019) to turn the database of badger GPS locations into tracks for each animal to extract movement data that would be used to parameterise badger movement in the ABM. Each badger had a unique ID in the database to identify the animal and each collar had an ID to track repeated deployments. To ensure that our parameters were extracted accurately, and since badgers do not leave the sett during the day, we transformed each nightly track night into a burst and gave each badger and nightly track a burst ID which would be used for the analysis. For each badger-night, we calculated the time (in minutes) between each step, the distance (in metres) between each step, and then used that data to calculate speed of movement. We calculated the relative turning angle to assess directionality of movement and the net squared displacement to assess the percentage of the population that were long range dispersers (Singh et al. 2016). To assess if any badgers dispersed over long ranges we used net squared displacement for the study period rather than for each night (Borger and Fryxell, 2012). Finally, we created unique databases for each class of animal (male, female, adult, juvenile) in order to assign the correct movement parameters to the appropriate class of badger.For each unique badger we also calculated their home range throughout the study period using the adehabitatHR package in R as a proxy for territory (Calenge and Fortmann-Roe, 2023). Space use for each individual badger was estimated through Kernel density estimation (KDE) models, and the 95% contour was used to indicate each badger’s home range, indicating where the badger is most likely to spend its time (ref here). The area of each polygon, measured in hectares (ha), was computed to quantify the area of the home range for each individual badger. Thest_intersection function from the sf package (Pebesma and Bivand, 2023) was used to intersect the CORINE habitats data with each badger’s home range to assess the presence, diversity and area of habitats within each badger’s home range. Using these data we conducted basic resource selection function (RSF) analysis (Manly et al. 2007). We computed the availability of each habitat type within badger home ranges and calculated the proportion of GPS fixes within each habitat type during overall survey period, and the summer, and winter periods specifically to assess seasonal changes in habitat selection. These proportions were standardised by availability to compute selection ratios, indicating relative habitat preferences of badgers in each survey area. These selection ratios were used in the ABM to inform badger habitat selection.Spatial data analysis (sites generation) Each study site represented a clearfell area in Ireland, identified by selecting 100 clearfell sites from a database of 1,939 clearfell events in the Coillte Harvest data. For each site, the centroid of the clearfell area was calculated and buffered by 5 km, resulting in a total site area of approximately 78.54 km². To incorporate relevant spatial information, we used the st_intersection function to create spatial databases for each site, integrating key data layers such as farm boundaries from the LPIS system, habitat information from CORINE, main badger setts from DAFM wildlife surveys, and clearfell boundaries from Coillte Harvest. These spatial databases were then transformed into shapefiles, which were included in the agent-based model (ABM) for simulating wildlife movement and bTB risk dynamics.Simulation (Agent Based Model) We built a spatially and temporally explicit agent-based model, also referred to as individual-based model, called an ABM hereafter, in the Netlogo environment to assess the behavioural response of badgers to clearfell forestry activities taking place in an agro-forestry mosaic. The purpose of our ABM was to simulate badger movement after a clearfell and calculate the number of alien farm encounters (i.e. entering farms that fall outside of their own territory) from badgers on 100 real, heterogeneous landscapes and to record encounter rates by badgers and by farm (using the herd number of every unique farm) to understand the ecological role of badgers in elevated disease risk for farms located close to clearfells hypothesised in previous studies (see Murphy et al. 2022, Byrne et al. 2022, Khouri et al. 2023).We expected the role of badger density, and habitat selection for more favourable environments (hypothesised by Murphy et al. 2022) to cause higher encounter rates after a clearfell. To consider our model sufficient for its purpose, all parameters were either defined from real world GPS or spatial data and any parameter that was derived from the literature was subjected to sensitivity analysis to ensure that behaviours replicated what expert stakeholders expected to see in the field. The ABM incorporated key badger behaviours such as seasonality of activity, home ranges, resource selection, dispersal, mating and mortality, with the aim of modelling encounter rates and locations that may be representative of disease transmission on a heterogeneous landscape. State variables for the spatial patches and for the badger are given in the ODD protocol.We ran 25 models (optimal number of models was decided by sensitivity analysis – whereby the number of encounters over 1,5,10,25,50,100 models were assessed and 25 models was when the estimate stabilised and was hence chosen) for each site (100 sites x 25 models, 2500 simulations in total) each lasting one calendar year after clearfell (based on top performing models in Murphy et al. 2022). Each model output included site level statistics and farm level statistics, to assess both the macro-ecological variables and the relative impact at a farm level. Model development is described comprehensively using the standardised reporting ODD protocol proposed by Grimm et al (2010) in Supplementary Material 1. See Fig. 1 for an example of the modelling framework and see Fig. 2 for locations of all 100 sites and the size in hectares of the clearfell included.   The simulation generated a range of variables that allowed us to assess the impact of clearfell forestry on badger movement, population dynamics, and dispersal patterns. Each simulation run was replicated across multiple sites, with the replicate number recorded to ensure comparisons within sites. Sett and badger densities were recorded for each model run, both of which were calculated per km2at the end of the simulation year. These values provided a snapshot of the spatial distribution of badgers and their setts within each study area. In addition to these final densities, the simulation tracked changes in both badger and sett densities over the study period. This allowed for an assessment of how populations fluctuated from the start to the end of the simulation year, offering insights into population stability or growth in response to forestry disturbances. Two critical dispersal metrics were also captured: the total number of badgers dispersing to new territories and the subset of individuals that engaged in long-distance dispersal, defined as those moving beyond neighbouring territories to join or establish new clans. We also measured the direct disturbance to the badger population, measured by the number of individuals disturbed by clearfell activities. The county in which the site was located was also saved for each site. These variables collectively provided a comprehensive view of how clearfell forestry affects badger populations, ranging from local population density changes to broader landscape-level dispersal patterns within the subsequent modelling exercise, see below.

The urea nitrogen salvaging process (UNS) supports the symbiotic relationship between ruminants and their gastrointestinal microbiome by both supplying nitrogen and buffering bacterially-derived short chain fatty acids (SCFAs). Studying such physiological processes via investigation of rumen papillae morphology and functioning, as well as western blotting to detect rumen urea transporters, allows researchers to test hypotheses linking physiology and ecology. The goal of this innovative approach is to indicate the way forward for future research. Our previous studies have shown the importance of the UT-B2 urea transporters in the rumen of wild fallow deer living in Phoenix Park, Dublin. In this current pilot study, we investigated the effects on these transporters of seasonal changes – linked to changes in feeding intake during and after the mating season - and acceptance of artificial food from park visitors in adult male bucks. Investigation of the rumen papillae revealed that animals culled in January had significantly longer papillae than those culled just after the rutting season in November, when bucks interrupted feeding from a few days to weeks. In contrast, western blotting analysis showed that there was no significant difference in the abundance of UT-B2 transporters between these two groups. Adult males that had displayed consistent begging behaviour to obtain food from human visitors to the park had a higher papillae density. Furthermore, these animals had a significantly higher abundance of UT-B2 transporters, which was shown by immunolocalization studies to be predominantly in the stratum basale layer of the rumen papillae. Our research suggests that human-wildlife feeding interactions can have subtle effects on the physiology of individual animals involved. The findings of this novel study therefore improve our understanding of basic rumen physiological processes, but also add insight into the unseen effects that humans feeding wildlife may have.

The conservation and management of large carnivores is a challenging task for researchers seeking to foster human-wildlife coexistence. Agent-based models (ABMs) allow researchers to design realistic simulations of their study system, including environmental, anthropogenic and ecological agents and their characteristics to examine interactions at landscape scales and investigate how interventions may alter potential outcomes. Including high-resolution Geographic Information Systems (GIS) data and real-world ecological data streams in ABMs represents an innovative approach for site-specific investigations into how best to manage the return of large carnivores. We used GIS-integrated ABMs to study the outcome of wolf reintroduction to Ireland’s national parks with respect to wolf ecology and wolf-livestock interactions. We introduced management strategies and policy interventions to assess how wolf-livestock interactions could be influenced by wildlife managers and whether outcomes were site-specific. Our study found that wolves could persist past the initial introduction in each protected area regardless of which reintroduction strategy is utilised, however, human-wildlife conflict warning signs emerged. Wolves extensively disperse outside protected areas, den-sites are located close (c. 1.5km) to park boundaries and livestock-depredations do occur. Management and policy interventions significantly reduced the likelihood of human-wildlife conflict by reducing the number of livestock depredations and creating ecological buffers that reduce wolf-human interactions, however, the individual characteristics of the protected area determined the success of each management and policy intervention. This analysis reveals nuanced differences in the response of each study area to the same management and policy interventions, demonstrating that the outcome of management and policy interventions is highly dependent on specific ecological conditions captured in GIS data. This underscores the importance of integrating high-resolution GIS data into ecological ABMs and the power that such integration can bring to these models for delivering tailored recommendations to decision-makers enabling human-wildlife coexistence with large carnivores in complex landscapes.

Wildlife population dynamics are modulated by abiotic and biotic factors, typically climate, resource availability, density-dependent effects, and predator-prey interactions. Understanding if human-caused disturbances shape these processes is needed for the conservation and management of ecological communities within increasingly human-dominated landscapes. Garnering this understanding is difficult due to the lack of long-term longitudinal data on wildlife populations. Concurrently, the interplay between long-term human-mediated disturbances, climate, and predator density on ungulate population dynamics has been under-studied. Using a 50-year time series (1962-2012) on mule deer (Odocoileus hemionus) demographics, seasonal weather, predator density, and oil and gas development patterns from the North Dakota Badlands to investigate the long-term effects of landscape-level disturbance, we aimed to evaluate if harsh weather conditions in-combination with energy development and predators affected fall mule deer recruitment. We found that density-dependent effects and harsh seasonal weather primarily drove recruitment in the North Dakota Badlands. Recruitment was further shaped by interacting effects of harsh seasonal weather and predator presence in the form of high coyote density. Additionally, we found that fall recruitment was subtly modulated by interactions between seasonal weather and energy development (i.e., lower recruitment when harsher weather was combined with higher density of active oil and gas wells), and that the combined effect of predator density and energy development was not interactive but rather additive. Our analysis demonstrates the effect of energy development by modulating mule deer recruitment fluctuations concurrent with main recruitment drivers being biotic (density-dependency, habitat, predation) and abiotic (harsh seasonal weather, woody vegetation encroachment). A pattern emerges of density dependence, presumably due to limited quality habitat, being the primary factor influencing fall fawn recruitment in mule deer. Secondarily, stochastic weather events periodically cause dramatic declines in recruitment. Finally, the interactions between human disturbance and predation can be additive to the aforementioned drivers of recruitment and subsequently cause further declines.

Recent debate has focused on whether variation in personality primarily reflects variation in resource allocation or resource acquisition of individuals. These two mechanisms differ in their expected patterns between personality and survival. If personality mainly reflects variation in resource allocation, then bold (i.e. risk-taking) individuals are expected to live shorter, whereas the opposite pattern is expected with resource acquisition. Here we studied the relationship between neonate personality and early-life survival in 269 juveniles of a population of fallow deer (Dama dama). We found that bolder individuals were more likely to survive, and therefore, paid no apparent survival cost. Interestingly, the physiological but not the behavioural dimension of neonate boldness was directly linked to survival, suggesting that these individuals may be of higher state than their shyer counterparts. As the first study linking neonate personality to survival in a free-ranging mammal, we provide novel insights into drivers behind early-life individual variation.