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Every year, there are millions of documented vehicle collisions involving cervids across Europe and North America. While temporal patterns in collision occurrence are relatively well described, few studies have targeted deer behaviour as a critical component of collision prevention. In this study, we investigated weekly and daily patterns in road crossing behaviour in roe deer. Using road crossing events and movement data obtained from GPS telemetry, we employed mixed-effect models to explain frequency and timing of crossings at five road segments by a number of predictors including traffic volume, deer movement activity and the presence of wildlife warning reflectors. We analysed 13,689 road crossing events by 32 study animals. Individual variation in crossing frequency was high but daily patterns in crossing events were highly consistent among animals. Variation in the intensity of movement activity on a daily and seasonal scale was the main driver of road crossing behaviour. The seasonal variation in crossing frequency reflected differences in movement activity throughout the reproductive cycle, while daily variation in the probability to cross exhibited a clear nocturnal emphasis and reflected crepuscular activity peaks. The frequency of road crossings increased as a function of road density in the home-range, while traffic volume only exerted marginal effects. Movement activity of roe deer in our study coincided with commuter traffic mainly in the early morning and late afternoon during winter and during periods of high spatial activity such as the rut. Both timing and frequency of crossing events remained unchanged in the presence of reflectors. Our results emphasise the importance of behavioural studies for understanding roe deer vehicle-collision patterns and thus provide important information for collision prevention. We suggest that mitigation of collision risk should focus on strategic seasonal measures and animal warning systems targeting drivers.

Background Our understanding of movement patterns and behaviours of wildlife has advanced greatly through the use of improved tracking technologies, including application of accelerometry (ACC) across a wide range of taxa. However, most ACC studies either use intermittent sampling that hinders continuity or continuous data logging relying on tracker retrieval for data downloading which is not applicable for long term study. To allow long-term, fine-scale behavioural research, we evaluated a range of machine learning methods for their suitability for continuous on-board classification of ACC data into behaviour categories prior to data transmission. Methods We tested six supervised machine learning methods, including linear discriminant analysis (LDA), decision tree (DT), support vector machine (SVM), artificial neural network (ANN), random forest (RF) and extreme gradient boosting (XGBoost) to classify behaviour using ACC data from three bird species (white stork Ciconia ciconia , griffon vulture Gyps fulvus and common crane Grus grus ) and two mammals (dairy cow Bos taurus and roe deer Capreolus capreolus ). Results Using a range of quality criteria, SVM, ANN, RF and XGBoost performed well in determining behaviour from ACC data and their good performance appeared little affected when greatly reducing the number of input features for model training. On-board runtime and storage-requirement tests showed that notably ANN, RF and XGBoost would make suitable on-board classifiers. Conclusions Our identification of using feature reduction in combination with ANN, RF and XGBoost as suitable methods for on-board behavioural classification of continuous ACC data has considerable potential to benefit movement ecology and behavioural research, wildlife conservation and livestock husbandry.

Camera traps have become an important tool in wildlife monitoring. However, an issue in interpreting their data in statistical analyses of population densities, demography or behaviour is that the probability of detecting the target animals and their behaviours may vary depending on environmental and methodological factors. A specific problem is the type of flash used in the camera trap, as animals may react differently to different flash types and change their avoidance or habituation behaviour accordingly over time. Here, we provide the first systematic test of the impact of flash type in studies of red deer (Cervus elaphus) and roe deer (Capreolus capreolus), based on an analysis of behavioural responses to white, standard infrared and black flashes during 900 camera trap deployments in the Bavarian Forest National Park and the Northern Black Forest. The results revealed that both deer species were more likely to react to standard infrared than to black flash, but trigger delays prevented comparisons to white flash. Red deer reacted more frequently to camera traps than did roe deer, and responses were more common in the Northern Black Forest than in the Bavarian Forest National Park, where hunting is severely restricted. Contrary to our expectations, camera trapping rates of free‐ranging deer did not significantly decline over time for any flash type or species. Despite the lack of evidence for avoidance behaviour, we recommend the use of black flash for behavioural studies of deer populations to minimize the risk of introducing a source of disturbance whereas infrared and white flash are equally suitable for determinations of demographic parameters. We analysed the behavioural responses of red deer and roe deer to white, standard infrared and black flashes during 900 camera trap deployments in two different study areas. Both species responded more frequently to standard infrared flash than to black flash, but roe deer showed considerably fewer reactions than red deer. Irrespective of flash type and species, we found no evidence for camera trap avoidance.

Humans, as super predators, can have strong effects on wildlife behaviour, including profound modifications of diel activity patterns. Subsequent to the return of large carnivores to human‐modified ecosystems, many prey species have adjusted their spatial behaviour to the contrasting landscapes of fear generated by both their natural predators and anthropogenic pressures. The effects of predation risk on temporal shifts in diel activity of prey, however, remain largely unexplored in human‐dominated landscapes. We investigated the influence of the density of lynx Lynx lynx, a nocturnal predator, on the diel activity patterns of their main prey, the roe deer Capreolus capreolus, across a gradient of human disturbance and hunting at the European scale. Based on 11 million activity records from 431 individually GPS‐monitored roe deer in 12 populations within the EURODEER network (http://eurodeer.org), we investigated how lynx predation risk in combination with both lethal and non‐lethal human activities affected the diurnality of deer. We demonstrated marked plasticity in roe deer diel activity patterns in response to spatio‐temporal variations in risk, mostly due to human activities. In particular, roe deer decreased their level of diurnality by a factor of 1.37 when the background level of general human disturbance was high. Hunting exacerbated this effect, as during the hunting season deer switched most of their activity to night‐time and, to a lesser extent, to dawn, although this pattern varied noticeably in relation to lynx density. Indeed, in the presence of lynx, their main natural predator, roe deer were relatively more diurnal. Overall, our results revealed a strong influence of human activities and the presence of lynx on diel shifts in roe deer activity. In the context of the recovery of large carnivores across Europe, we provide important insights about the effects of predators on the behavioural responses of their prey in human‐dominated ecosystems. Modifications in the temporal partitioning of ungulate activity as a response to human activities may facilitate human–wildlife coexistence, but likely also have knock‐on effects for predator–prey interactions, with cascading effects on ecosystem functioning. Translated Résumé Les humains, en tant que ‘super‐prédateurs’, peuvent avoir des effets importants sur le comportement de la faune sauvage, y compris des modifications profondes de leurs rythmes circadiens d'activité. A la suite du retour des grands carnivores dans les écosystèmes anthropisés, de nombreuses espèces proies ont ajusté leur comportement spatial à ces paysages de la peur contrastés, générés à la fois par les pressions liées aux risques anthropiques et à la présence de leurs prédateurs naturels. Les effets du risque de prédation sur les modifications temporelles des rythmes circadiens d'activité des proies restent cependant largement inconnus dans les écosystèmes dominés par l'homme. Ici, nous avons étudié l'influence de la densité de lynx Lynx lynx, un prédateur nocturne, sur les rythmes circadiens d'activité de leur proie principale, le chevreuil Capreolus capreolus, à travers un gradient de pressions anthropiques à l’échelle Européenne. Sur la base de plus de 11 million de données d'activité issues de 431 suivis individuels de chevreuils équipés de colliers GPS provenant de 12 populations au sein du réseau EURODEER (http://eurodeer.org), nous avons analysé comment le risque de prédation par le lynx, associé aux risques létaux et non‐létaux des activités humaines, influence la diurnalité des chevreuils. Nous avons démontré une forte plasticité des rythmes circadiens d'activité des chevreuils en réponse aux variations spatio‐temporelles du risque, et notamment face aux activités humaines. Plus particulièrement, les chevreuils diminuent leur degré de diurnalité d'un facteur de 1.37 lorsque le dérangement humain est important. La chasse accentue cet effet, puisque durant la saison de chasse les chevreuils basculent la plupart de leur activité de nuit, et dans une moindre mesure, durant l'aube également, bien que ce patron soit essentiellement variable en fonction de la densité de lynx. En effet, en présence de lynx, leur principal prédateur, les chevreuils sont relativement plus diurnes. Globalement, nos résultats révèlent une forte influence des activités humaines et de la présence de lynx sur l'ajustement des rythmes circadiens d'activité des chevreuils. Dans le contexte du retour des grands carnivores en Europe, notre étude apporte de nouvelles connaissances sur les effets des prédateurs sur la réponse comportementale de leur proie dans des écosystèmes anthropisés. La modification de la répartition temporelle de l'activité des ongulés en réponse aux activités humaines pourrait être un facteur facilitant la coexistence homme‐faune sauvage, avec toutefois des conséquences autres sur les interactions prédateurs‐proies et leurs effets en cascade sur le fonctionnement des écosystèmes. The authors compared diel activity of roe deer across 12 European populations. Deer were more nocturnal when human disturbance was high, but more diurnal in the presence of lynx, their main natural predator. Modifications in the temporal partitioning of ungulate activity likely have knock‐on effects for a variety of ecological processes. Photo credit: Nicolas Cèbe (CEFS‐INRA).

Background Biotelemetry offers an increasing set of tools to monitor animals. Acceleration sensors in particular can provide remote observations of animal behavior at high temporal resolution. While recent studies have demonstrated the capability of this technique for a wide range of species and behaviors, a coherent methodology is still missing (1) for behavior monitoring of large herbivores that are usually tagged with neck collars and frequently switch between diverse behaviors and (2) for monitoring of vigilance behavior. Here, we present an approach that aims at remotely monitoring different types of large herbivore behavior including vigilance with acceleration data. Methods We pioneered this approach with field observations of eight collared roe deer ( Capreolus capreolus ). First, we trained a classification model for distinguishing seven structural behavior categories: lying, standing, browsing, walking, trotting, galloping and ‘others’. Second, we developed a model that predicted the internal states, active and resting, based on the predicted sequence of structural behaviors and expert-based rules. Further, we applied both models to automatically monitor vigilance behavior and compared model predictions with expert judgment of vigilance behavior. To exemplify the practical application of this approach, we predicted behavior, internal state and vigilance continuously for a collared roe deer. Results The structural behaviors were predicted with high accuracy (overall cross-validated accuracy 71%). Only behaviors that are similar in terms of posture and dynamic body movements were prone to misclassification. Active and resting states showed clear distinction and could be utilized as behavioral context for the detection of vigilance behavior. Here, model predictions were characterized by excellent consistency with expert judgment of vigilance behavior (mean accuracy 96%). Conclusion In this study, we demonstrated the strong potential and practical applicability of acceleration data for continuous, high-resolution behavior monitoring of large herbivores and showed that vigilance behavior is well detectable. In particular, when combined with spatial data, automated behavior recognition will enrich many fields in behavioral ecology by providing extensive access to behaviors of animals in the wild.

Wildlife tagging provides critical insights into animal movement ecology, physiology, and behavior amid global ecosystem changes. However, the stress induced by capture, handling, and tagging can impact post-release locomotion and activity and, consequently, the interpretation of study results. Here, we analyze post-tagging effects on 1585 individuals of 42 terrestrial mammal species using collar-collected GPS and accelerometer data. Species-specific displacements and overall dynamic body acceleration, as a proxy for activity, were assessed over 20 days post-release to quantify disturbance intensity, recovery duration, and speed. Differences were evaluated, considering species-specific traits and the human footprint of the study region. Over 70% of the analyzed species exhibited significant behavioral changes following collaring events. Herbivores traveled farther with variable activity reactions, while omnivores and carnivores were initially less active and mobile. Recovery duration proved brief, with alterations diminishing within 4–7 tracking days for most species. Herbivores, particularly males, showed quicker displacement recovery (4 days) but slower activity recovery (7 days). Individuals in high human footprint areas displayed faster recovery, indicating adaptation to human disturbance. Our findings emphasize the necessity of extending tracking periods beyond 1 week and particular caution in remote study areas or herbivore-focused research, specifically in smaller mammals.

Every year, there are millions of documented vehicle collisions involving cervids across Europe and North America. While temporal patterns in collision occurrence are relatively well described, few studies have targeted deer behaviour as a critical component of collision prevention. In this study, we investigated weekly and daily patterns in road crossing behaviour in roe deer. Using road crossing events and movement data obtained from GPS telemetry, we employed mixed-effect models to explain frequency and timing of crossings at five road segments by a number of predictors including traffic volume, deer movement activity and the presence of wildlife warning reflectors. We analysed 13,689 road crossing events by 32 study animals. Individual variation in crossing frequency was high but daily patterns in crossing events were highly consistent among animals. Variation in the intensity of movement activity on a daily and seasonal scale was the main driver of road crossing behaviour. The seasonal variation in crossing frequency reflected differences in movement activity throughout the reproductive cycle, while daily variation in the probability to cross exhibited a clear nocturnal emphasis and reflected crepuscular activity peaks. The frequency of road crossings increased as a function of road density in the home-range, while traffic volume only exerted marginal effects. Movement activity of roe deer in our study coincided with commuter traffic mainly in the early morning and late afternoon during winter and during periods of high spatial activity such as the rut. Both timing and frequency of crossing events remained unchanged in the presence of reflectors. Our results emphasise the importance of behavioural studies for understanding roe deer vehicle-collision patterns and thus provide important information for collision prevention. We suggest that mitigation of collision risk should focus on strategic seasonal measures and animal warning systems targeting drivers.

Millions of animals are killed by vehicle collisions each year. As mitigation measures, wildlife warning reflectors have become increasingly popular, although clear evidence for their effectiveness is lacking. A reason for inconclusive results in the literature may be that most previous studies on the effectiveness of wildlife warning reflectors compare animal-vehicle collision rates with and without reflectors, a setting characterised by low event rates and weak experimental control. Animal behaviour can be expected to provide a more direct evidence for a possible effect of reflectors. In this study, we analyse roe deer behaviour in the presence of a blue semicircle reflector, one of the most frequently applied wildlife warning reflectors in Germany and other parts of Europe. Behavioural response to these reflectors (classified as no reaction, vigilance, short-distance flight and long-distance flight) was recorded both under controlled experimental conditions with captive roe deer and for free-ranging roe deer at road sections with traffic occurrence. We used generalised linear mixed models (GLMMs) to test if reflector presence induced threat-related behaviour (vigilance, flight) and movement away from the reflectors. We found no significant evidence that the light stimulus emitted by reflectors was perceived as a threat or induced evasive movement. We conclude that our study provides no evidence that blue semicircle reflectors induce behaviour in roe deer that seems suitable to reduce roe deer-vehicle collisions.

ContextThe Complementary Habitat Hypothesis posits that animals access resources for different needs by moving between complementary habitats that can be seen as ‘resource composites’. These movements can occur over a range of temporal scales, from diurnal to seasonal, in response to multiple drivers such as access to food, weather constraints, risk avoidance and human disturbance. Within this framework, we hypothesised that large herbivores cope with human-altered landscapes through the alternate use of complementary habitats at both daily and seasonal scales.ObjectivesWe tested the Complementary Habitat Hypothesis in European roe deer (Capreolus capreolus) by classifying 3900 habitat-annotated movement trajectories of 154 GPS-monitored individuals across contrasting landscapes.MethodsWe considered day-night alternation between open food-rich and closed refuge habitats as a measure of complementary habitat use. We first identified day–night alternation using the Individual Movement - Sequence Analysis Method, then we modelled the proportion of day–night alternation over the year in relation to population and individual characteristics.ResultsWe found that day-night alternation is a widespread behaviour in roe deer, even across markedly different landscapes. Day–night alternation followed seasonal trends in all populations, partly linked to vegetation phenology. Within populations, seasonal patterns of open/closed habitat alternation differed between male and female adults, but not in juveniles.ConclusionOur results support the Complementary Habitat Hypothesis by showing that roe deer adjust their access to the varied resources available in complex landscapes by including different habitats within their home range, and sequentially alternating between them in response to seasonal changes and individual life history.

In most studies that estimate abundance of foxes from faeces counts, scanning is done along trails and roads or along linear features such as hedges, because it is supposed that foxes defecate mainly along these structures. As a consequence, only part (i.e. trails or linear features) of the total habitat is searched and results are possibly biased if usage by foxes of these searched features is subject to spatial or temporal variation. We therefore investigated three methods for counting red fox Vulpes vulpes faeces, that differ in the shape of the sampling units: trails and two alternatives; i.e. transects and squares. We searched for faeces using these three methods in two study areas (the Upper Rhine Valley and the Black Forest valleys) at 61 study plots and found a total of 257 fox faeces. Methods for estimating abundance should ideally have high accuracy and high precision. As actual fox densities in the areas were unknown, we were unable to assess the accuracy of our sampling methods and thus focused on method precision. We fit separate negative binomial regression models for each method with the number of faeces found as the dependent variable and a set of landscape variables as possible explanatory variables. The transect method detected significant differences in the number of faeces found between the study areas and was most precise. Even though we did find more faeces with the trail method, the precision of this method was lower than that of the transect method. For the methods trail and square, variance in the number of faeces found was large in comparison to their mean. Bias caused by methods that only sample part of the habitat is not limited to faecal counts and red fox studies, but can also occur with other species and methods.