The ongoing recovery of terrestrial large carnivores in North America and Europe is accompanied by intense controversy. On the one hand, reestablishment of large carnivores entails a recovery of their most important ecological role, predation. On the other hand, societies are struggling to relearn how to live with apex predators that kill livestock, compete for game species, and occasionally injure or kill people. Those responsible for managing these species and mitigating conflict often lack fundamental information due to a long-standing challenge in ecology: How do we draw robust population-level inferences for elusive animals spread over immense areas? Here we showcase the application of an effective tool for spatially explicit tracking and forecasting of wildlife population dynamics at scales that are relevant to management and conservation. We analyzed the world’s largest dataset on carnivores comprising more than 35,000 noninvasively obtained DNA samples from over 6,000 individual brown bears (Ursus arctos), gray wolves (Canis lupus), and wolverines (Gulo gulo). Our analyses took into account that not all individuals are detected and, even if detected, their fates are not always known. We show unequivocal quantitative evidence of large carnivore recovery in northern Europe, juxtaposed with the finding that humans are the single-most important factor driving the dynamics of these apex predators. We present maps and forecasts of the spatiotemporal dynamics of large carnivore populations, transcending national boundaries and management regimes.

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Abstract. Interspecific competition can influence the distribution and abundance of species and the structure of ecological communities and entire ecosystems. Interactions between apex predators can have cascading effects through the entire natural community, which supports broadening the scope of conservation from single species to a much wider ecosystem perspective. However, competition between wild large carnivores can hardly be measured experimentally. In this study, we analyzed the expansion of the Scandinavian wolf (Canis lupus) population during its recovery from the early 1990s. We took into account wolf-, habitat-, human-and brown bear (Ursus arctos)-related factors, because wolf expansion occurred within an area partially sympatric with bears. Wolf pair establishment was positively related to previous wolf presence and was negatively related to road density, distance to other wolf territories, and bear density. These findings suggest that both human-related habitat modification and interspecific competition have been influential factors modulating the expansion of the wolf population. Interactions between large carnivores have the potential to affect overall biodiversity. Therefore, conservation-oriented management of such species should consider interspecific interactions, rather than focusing only on target populations of single species. Long-term monitoring data across large areas should also help quantify and predict the influence of biotic interactions on species assemblages and distributions elsewhere. This is important because interactive processes can be essential in the regulation, stability, and resilience of ecological communities.

Trophic interactions are a fundamental topic in ecology, but we know little about how competition between apex predators affects predation, the mechanism driving top-down forcing in ecosystems. We used long-term datasets from Scandinavia (Europe) and Yellowstone National Park (North America) to evaluate how grey wolf (Canis lupus) kill rate was affected by a sympatric apex predator, the brown bear (Ursus arctos). We used kill interval (i.e. the number of days between consecutive ungulate kills) as a proxy of kill rate. Although brown bears can monopolize wolf kills, we found no support in either study system for the common assumption that they cause wolves to kill more often. On the contrary, our results showed the opposite effect. In Scandinavia, wolf packs sympatric with brown bears killed less often than allopatric packs during both spring (after bear den emergence) and summer. Similarly, the presence of bears at wolf-killed ungulates was associated with wolves killing less often during summer in Yellowstone. The consistency in results between the two systems suggests that brown bear presence actually reduces wolf kill rate. Our results suggest that the influence of predation on lower trophic levels may depend on the composition of predator communities.

Summary For socially monogamous species, breeder bond dissolution has important consequences for population dynamics, but the extent to which extrinsic or intrinsic population factors causes pair dissolution remain poorly understood, especially among carnivores.Using an extensive life‐history data set, a survival analysis and competing risks framework, we examined the fate of 153 different wolf (Canis lupus) pairs in the recolonizing Scandinavian wolf population, during 14 winters of snow tracking and DNA monitoring.Wolf pair dissolution was generally linked to a mortality event and was strongly affected by extrinsic (i.e. anthropogenic) causes. No divorce was observed, and among the pair dissolution where causes have been identified, death of one or both wolves was always involved. Median time from pair formation to pair dissolution was three consecutive winters (i.e. approximately 2 years). Pair dissolution was mostly human‐related, primarily caused by legal control actions (36·7%), verified poaching (9·2%) and traffic‐related causes (2·1%). Intrinsic factors, such as disease and age, accounted for only 7·7% of pair dissolutions. The remaining 44·3% of dissolution events were from unknown causes, but we argue that a large portion could be explained by an additional source of human‐caused mortality, cryptic poaching.Extrinsic population factors, such as variables describing the geographical location of the pair, had a stronger effect on risk of pair dissolution compared to anthropogenic landscape characteristics. Population intrinsic factors, such as the inbreeding coefficient of the male pair member, had a negative effect on pair bond duration. The mechanism behind this result remains unknown, but might be explained by lower survival of inbred males or more complex inbreeding effects mediated by behaviour.Our study provides quantitative estimates of breeder bond duration in a social carnivore and highlights the effect of extrinsic (i.e. anthropogenic) and intrinsic factors (i.e. inbreeding) involved in wolf pair bond duration. Unlike the effects of intrinsic and extrinsic factors that are commonly reported on individual survival or population growth, here we provide quantitative estimates of their potential effect on the social unit of the population, the wolf pair.

1. Spatial capture-recapture (SCR) is an increasingly popular method for estimating ecological parameters. SCR often relies on data collected over relatively long sampling periods. While longer sampling periods can yield larger sample sizes and thus increase the precision of estimates, they also increase the risk of violating the closure assumption, thereby potentially introducing bias. The sampling period characteristics are therefore likely to play an important role in this bias-precision trade-off.Yet few studies have studied this trade-off and none has done so for SCR models.2. In this study, we explored the influence of the length and timing of the sampling period on the bias-precision trade-off of SCR population size estimators. Using a continuous time-to-event approach, we simulated populations with a wide range of life histories and sampling periods before quantifying the bias and precision of population size estimates returned by SCR models.3. While longer sampling periods benefit the study of slow-living species (increased precision and lower bias), they lead to pronounced overestimation of population size for fast-living species. In addition, we show that both bias and uncertainty increase when the sampling period overlaps the reproductive season of the study species. 4. Based on our findings, we encourage investigators to carefully consider the life history of their study species when contemplating the length and the timing of the sampling period. We argue that both spatial and non-spatial capture-recapture studies can safely extend the sampling period to increase precision, as long as it is timed to avoid peak recruitment periods. The simulation framework we propose here can be used to guide decisions regarding the sampling period for a specific situation.

Spatial capture–recapture models (SCR) are used to estimate animal density and to investigate a range of problems in spatial ecology that cannot be addressed with traditional nonspatial methods. Bayesian approaches in particular offer tremendous flexibility for SCR modeling. Increasingly, SCR data are being collected over very large spatial extents making analysis computational intensive, sometimes prohibitively so. To mitigate the computational burden of large‐scale SCR models, we developed an improved formulation of the Bayesian SCR model that uses local evaluation of the individual state‐space (LESS). Based on prior knowledge about a species’ home range size, we created square evaluation windows that restrict the spatial domain in which an individual's detection probability (detector window) and activity center location (AC window) are estimated. We used simulations and empirical data analyses to assess the performance and bias of SCR with LESS. LESS produced unbiased estimates of SCR parameters when the AC window width was ≥5σ (σ: the scale parameter of the half‐normal detection function), and when the detector window extended beyond the edge of the AC window by 2σ. Importantly, LESS considerably decreased the computation time needed for fitting SCR models. In our simulations, LESS increased the computation speed of SCR models up to 57‐fold. We demonstrate the power of this new approach by mapping the density of an elusive large carnivore—the wolverine (Gulo gulo)—with an unprecedented resolution and across the species’ entire range in Norway (> 200,000 km2). Our approach helps overcome a major computational obstacle to population and landscape‐level SCR analyses. The LESS implementation in a Bayesian framework makes the customization and fitting of SCR accessible for practitioners working at scales that are relevant for conservation and management.

Spatial capture–recapture (SCR) models are commonly used for analysing data collected using noninvasive genetic sampling (NGS). Opportunistic NGS often leads to detections that do not occur at discrete detector locations. Therefore, spatial aggregation of individual detections into fixed detectors (e.g., centre of grid cells) is an option to increase computing speed of SCR analyses. However, it may reduce precision and accuracy of parameter estimations. Using simulations, we explored the impact that spatial aggregation of detections has on a trade‐off between computing time and parameter precision and bias, under a range of biological conditions. We used three different observation models: the commonly used Poisson and Bernoulli models, as well as a novel way to partially aggregate detections (Partially Aggregated Binary model [PAB]) to reduce the loss of information after aggregating binary detections. The PAB model divides detectors into K subdetectors and models the frequency of subdetectors with more than one detection as a binomial response with a sample size of K. Finally, we demonstrate the consequences of aggregation and the use of the PAB model using NGS data from the monitoring of wolverine (Gulo gulo) in Norway. Spatial aggregation of detections, while reducing computation time, does indeed incur costs in terms of reduced precision and accuracy, especially for the parameters of the detection function. SCR models estimated abundance with a low bias (<10%) even at high degree of aggregation, but only for the Poisson and PAB models. Overall, the cost of aggregation is mitigated when using the Poisson and PAB models. At the same level of aggregation, the PAB observation model out‐performs the Bernoulli model in terms of accuracy of estimates, while offering the benefits of a binary observation model (less assumptions about the underlying ecological process) over the count‐based model. We recommend that detector spacing after aggregation does not exceed 1.5 times the scale‐parameter of the detection function in order to limit bias. We recommend the use of the PAB observation model when performing spatial aggregation of binary data as it can mitigate the cost of aggregation, compared to the Bernoulli model.