The ongoing expansion of wolf (Canis lupus) populations has led to a growing demand for up-to-date abundance estimates. Non-invasive genetic sampling (NGS) is now widely used to monitor wolves, as it allows individual identification and abundance estimation without physically capturing individuals. However, NGS is resource-intensive, partly because of the wolf elusive behaviour and wide distribution, but also because of the cost of DNA analyses. Optimization of sampling strategies is therefore a requirement for the long-term sustainability of wolf monitoring programs. Using data from the 2020-2021 Italian Alpine wolf monitoring, we investigate how (i) reducing the number of samples genotyped, (ii) reducing the number of transects, and (iii) reducing the number of repetitions of each search transect, impacted spatial capture-recapture population size estimates. Our study revealed that a 25% reduction in the number of transects or, alternatively, a 50% reduction in the maximum number of repetitions yielded abundance estimates comparable to those obtained using the entire dataset. These modifications would result in a 2,046 km reduction in total transect length and 19,628 km reduction in total distance searched. Further reducing the number of transects resulted in up to 15% lower and up to 17% less precise abundance estimates. Reducing only the number of genotyped samples led to higher (5%) and less precise (20%) abundance estimates. Randomly subsampling genotyped samples reduced the number of detections per individual, whereas subsampling search transects resulted in a less pronounced decrease in both the total number of detections and individuals detected. Our work shows how it is possible to optimise wolf monitoring by reducing search effort while maintaining the quality of abundance estimates, by adopting a modelling framework that uses a first survey dataset. We further provide general guidelines on how to optimise sampling effort when using spatial capture-recapture in large-scale monitoring programmes.

Survival among juvenile ungulates is an important demographic trait affecting population dynamics. In many systems, juvenile ungulates experience mortality from large carnivores, hunter harvest and climate-related factors. These mortality sources often shift in importance both in space and time. While wolves (Canis lupus) predate on moose (Alces alces) throughout all seasons, brown bear (Ursus arctos) predation and human harvest happen primarily during early summer and fall, respectively. Hence, understanding how the mortality of juvenile moose is affected by predation, harvest and climate is crucial to adaptively managing populations and deciding sustainable harvest rates. We used data from 39 female moose in south-central Scandinavia to investigate the mortality of 77 calves in summer/fall and winter/spring, in relation to carnivore presence (defined as wolf presence and bear density), summer productivity, secondary road density, winter severity and migratory strategy (migratory versus resident) using logistic regressions. Summer mortality varied significantly between years but was not correlated to any of our covariates. In winter, calf mortality was higher with deeper snow in areas with wolves compared to areas without and increased more strongly with an increasing proportion of clearcuts/young forests in the presence of wolves compared to when wolves were absent. Lastly, increasing hunting risk was associated with higher calf mortality, and migratory females had higher calf mortality compared to stationary ones. Our study provides useful insight into mortality rates of moose calves coexisting with two large carnivores and with an intensive harvest pressure. Increasing our understanding of the mechanisms driving calf mortality both in summer and winter will become increasingly important if the populations of wolves and bears continue to expand and the moose population declines, and both summers and winters become warmer.