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Abstract Scavenging is an important part of food acquisition for many carnivore species that switch between scavenging and predation. In landscapes with anthropogenic impact, humans provide food that scavenging species can utilize. We quantified the magnitude of killing versus scavenging by gray wolves (Canis lupus) in Scandinavia where humans impact the ecosystem through hunter harvest, land use practices, and infrastructure. We investigated the cause of death of different animals utilized by wolves, and examined how the proportion of their consumption time spent scavenging was influenced by season, wolf social affiliation, level of inbreeding, density of moose (Alces alces) as their main prey, density of brown bear (Ursus arctos) as an intraguild competitor, and human density. We used data from 39 GPS‐collared wolves covering 3198 study days (2001–2019), including 14,205 feeding locations within space–time clusters, and 1362 carcasses utilized by wolves. Most carcasses were wolf‐killed (80.5%) while a small part had died from other natural causes (1.9%). The remaining had either anthropogenic mortality causes (4.7%), or the cause of death was unknown (12.9%). Time spent scavenging was higher during winter than during summer and autumn. Solitary wolves spent more time scavenging than pack‐living individuals, likely because individual hunting success is lower than pack success. Scavenging time increased with the mean inbreeding coefficient of the adult wolves, possibly indicating that more inbred individuals resort to scavenging, which requires less body strength. There was weak evidence for competition between wolves and brown bears as well as a positive relationship between human density and time spent scavenging. This study shows how both intrinsic and extrinsic factors drive wolf scavenging behavior, and that despite a high level of inbreeding and access to carrion of anthropogenic origin, wolves mainly utilized their own kills.

Abstract Landscape characteristics, seasonal changes in the environment, and daylight conditions influence space use and detection of prey and predators, resulting in spatiotemporal patterns of predation risk for the prey. When predators have different hunting modes, the combined effects of multiple predators are mediated by the physical landscape and can result in overlapping or contrasting patterns of predation risk. Humans have become super‐predators in many anthropogenic landscapes by harvesting game species and competing with large carnivores for prey. Here, we used the locations of wolf (Canis lupus)‐killed and hunter‐killed moose (Alces alces) in south‐central Scandinavia to investigate whether environmental and anthropogenic features influenced where wolves and hunters killed moose. We predicted that the combined effects of wolves and hunters would result in contrasting spatial risk patterns due to differences in hunting modes. We expected these contrasting spatial risk patterns also to differ temporally. During the hunting season, the probability of a wolf kill increased with distance to bogs, whereas it decreased with increasing building density and distance to clearcuts and young forests. After the hunting season, the probability of a wolf kill increased with increasing terrain ruggedness and decreased with increasing building density, distance to main roads, and distance to clearcuts and young forests. The probability of a hunter kill was highest closer to bogs, main and secondary roads, in less rugged terrain and in areas with lower building density. Hunters killed all moose during the day, whereas wolves killed most moose at night during and after the hunting season. Our findings suggest that environmental and anthropogenic features mediate hunting and wolf predation risk. Additionally, we found that hunter‐ and wolf‐killed moose exhibited contrasting spatial associations to landscape features, most likely due to the different hunting modes displayed by hunters and wolves. However, wolf predation and hunting risks also contrasted over time since wolves killed mostly at night and hunters were restricted to hunting during daytime and during the hunting season. This temporal segregation in risk might therefore suggest that moose could minimize risk exposure by taking advantage of spatiotemporally vacant hunting domains.

Abstract Estimating the contribution of demographic parameters to changes in population growth is essential for understanding why populations fluctuate. Integrated population models (IPMs) offer a possibility to estimate the contributions of additional demographic parameters, for which no data have been explicitly collected—typically immigration. Such parameters are often subsequently highlighted as important drivers of population growth. Yet, accuracy in estimating their temporal variation, and consequently their contribution to changes in population growth rate, has not been investigated. To quantify the magnitude and cause of potential biases when estimating the contribution of immigration using IPMs, we simulated data (using northern wheatear Oenanthe oenanthe population estimates) from controlled scenarios to examine potential biases and how they depend on IPM parameterization, formulation of priors, the level of temporal variation in immigration and sample size. We also used empirical data on populations with known rates of immigration: Soay sheep Ovis aries and Mauritius kestrel Falco punctatus with zero immigration and grey wolf Canis lupus in Scandinavia with near‐zero immigration. IPMs strongly overestimated the contribution of immigration to changes in population growth in scenarios when immigration was simulated with zero temporal variation (proportion of variance attributed to immigration = 63% for the more constrained formulation and real sample size) and in the wild populations, where the true number of immigrants was zero or near‐zero (kestrel 19.1%–98.2%, sheep 4.2%–36.1% and wolf 84.0%–99.2%). Although the estimation of the contribution of immigration in the simulation study became more accurate with increasing temporal variation and sample size, it was often not possible to distinguish between an accurate estimation from data with high temporal variation versus an overestimation from data with low temporal variation. Unrealistically, large sample sizes may be required to estimate the contribution of immigration well. To minimize the risk of overestimating the contribution of immigration (or any additional parameter) in IPMs, we recommend to: (a) look for evidence of variation in immigration before investigating its contribution to population growth, (b) simulate and model data for comparison to the real data and (c) use explicit data on immigration when possible.

Circadian rhythms result from adaptations to biotic and abiotic environmental conditions that cycle through the day, such as light, temperature, or temporal overlap between interacting species. At high latitudes, close to or beyond the polar circles, uninterrupted midsummer daylight may pose a challenge to the circadian rhythms of otherwise nocturnal species, such as eagle owls Bubo bubo. By non‐invasive field methods, we studied eagle owl activity in light of their interactions with their main prey the water vole Arvicola amphibius, and their competitor the white‐tailed eagle Haliaeetus albicilla during continuous midsummer daylight on open, treeless islands in coastal northern Norway. We evaluated circadian rhythms, temporal overlap, exposure, and spatial distribution. The owls maintained a nocturnal activity pattern, possibly because slightly dimmer light around midnight offered favourable hunting conditions. The eagles were active throughout the 24‐h period as opposed to the strictly diurnal rhythm reported elsewhere, thus increasing temporal overlap and the potential for interference competition between the two avian predators. This may indicate an asymmetry, with the owls facing the highest cost of interference competition. The presence of eagles combined with constant daylight in this open landscape may make the owls vulnerable to interspecific aggression, and contrary to the available literature, eagle owls rarely exposed themselves visually during territorial calls, possibly to avoid detection by eagles. We found indications of spatial segregation between owls and eagles reflecting differences in main prey, possibly in combination with habitat‐mediated avoidance. Eagle owl vocal activity peaked in the evening before a nocturnal peak in visual observations, when owls were active hunting, consistent with the hypothesis of a dusk chorus in nocturnal bird species. The owls may have had to trade‐off between calling and foraging during the few hours around midnight when slightly dimmer light reduced the detection risk while also providing better hunting conditions