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The ongoing expansion of wolf (Canis lupus) populations in Europe 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 due to the elusive behaviour and wide distribution of wolves, as well as the cost of DNA analyses. Optimisation 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 2046 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. [ABSTRACT FROM AUTHOR]

The impact of predation by wolves on prey populations was analysed in a portion of the western Alps characterized by a rich wild ungulate community. The number of wolves ranged from 7 to 15 (1.7-2.9 wolves/100 km2) during the study period (2000-2001). The diet of wolves mainly consisted of wild ungulates. Red deer and roe deer were the staple prey, while chamois was consumed less despite its high density. From 2000 to 2002, wolves annually removed 20-34 red deer, 21-58 roe deer, and 7-14 chamois per 100 km2. These amounts were equivalent to 19-51% of the annual mortality of red deer, 6-28% of roe deer and 6-9% of chamois. Additionally, hunting accounted for 58-94% of the annual mortality of red deer, 18-29% of roe deer and 22-43% of chamois. Other mortality factors (i.e. traffic accidents, disease, poaching) constituted a small percentage of the annual mortality of red deer (5-6%), roe deer (6-9%) and chamois (1%). During the study period, the density of prey animals was stable. Wolf predation did not seriously affect ungulate populations. The role of wolves on wild ungulate populations in the Susa Valley seemed to be compensatory. [ABSTRACT FROM AUTHOR]